Date: 2019-12-25 22:44:30 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 17331 136
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| ATC:kmeans | 2 | 1.000 | 0.986 | 0.994 | ** | |
| ATC:pam | 2 | 1.000 | 0.972 | 0.990 | ** | |
| CV:mclust | 2 | 1.000 | 0.958 | 0.980 | ** | |
| SD:skmeans | 3 | 0.999 | 0.977 | 0.989 | ** | 2 |
| ATC:hclust | 2 | 0.967 | 0.954 | 0.979 | ** | |
| MAD:skmeans | 3 | 0.949 | 0.926 | 0.972 | * | 2 |
| SD:pam | 2 | 0.949 | 0.946 | 0.969 | * | |
| SD:mclust | 3 | 0.944 | 0.927 | 0.971 | * | |
| SD:NMF | 4 | 0.944 | 0.884 | 0.954 | * | 2,3 |
| CV:NMF | 4 | 0.936 | 0.909 | 0.956 | * | 2,3 |
| MAD:NMF | 4 | 0.926 | 0.906 | 0.961 | * | 2,3 |
| SD:kmeans | 3 | 0.924 | 0.904 | 0.958 | * | |
| MAD:kmeans | 3 | 0.918 | 0.937 | 0.968 | * | 2 |
| ATC:skmeans | 3 | 0.913 | 0.911 | 0.961 | * | 2 |
| MAD:mclust | 4 | 0.904 | 0.840 | 0.930 | * | |
| MAD:pam | 2 | 0.895 | 0.923 | 0.968 | ||
| ATC:mclust | 3 | 0.877 | 0.883 | 0.952 | ||
| CV:kmeans | 3 | 0.864 | 0.899 | 0.946 | ||
| CV:pam | 3 | 0.859 | 0.922 | 0.962 | ||
| CV:hclust | 2 | 0.809 | 0.969 | 0.973 | ||
| CV:skmeans | 2 | 0.779 | 0.926 | 0.960 | ||
| ATC:NMF | 2 | 0.633 | 0.862 | 0.937 | ||
| SD:hclust | 4 | 0.551 | 0.669 | 0.823 | ||
| MAD:hclust | 2 | 0.313 | 0.701 | 0.853 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.969 0.947 0.978 0.494 0.503 0.503
#> CV:NMF 2 0.984 0.951 0.980 0.503 0.496 0.496
#> MAD:NMF 2 0.984 0.959 0.983 0.497 0.503 0.503
#> ATC:NMF 2 0.633 0.862 0.937 0.479 0.521 0.521
#> SD:skmeans 2 1.000 0.970 0.987 0.502 0.499 0.499
#> CV:skmeans 2 0.779 0.926 0.960 0.500 0.496 0.496
#> MAD:skmeans 2 0.969 0.946 0.978 0.503 0.498 0.498
#> ATC:skmeans 2 1.000 0.990 0.995 0.481 0.521 0.521
#> SD:mclust 2 0.539 0.929 0.935 0.339 0.688 0.688
#> CV:mclust 2 1.000 0.958 0.980 0.490 0.512 0.512
#> MAD:mclust 2 0.402 0.904 0.905 0.334 0.688 0.688
#> ATC:mclust 2 0.784 0.876 0.948 0.415 0.564 0.564
#> SD:kmeans 2 0.751 0.875 0.945 0.469 0.515 0.515
#> CV:kmeans 2 0.896 0.903 0.960 0.303 0.707 0.707
#> MAD:kmeans 2 0.939 0.934 0.974 0.493 0.507 0.507
#> ATC:kmeans 2 1.000 0.986 0.994 0.369 0.637 0.637
#> SD:pam 2 0.949 0.946 0.969 0.492 0.503 0.503
#> CV:pam 2 0.687 0.858 0.922 0.488 0.502 0.502
#> MAD:pam 2 0.895 0.923 0.968 0.489 0.505 0.505
#> ATC:pam 2 1.000 0.972 0.990 0.341 0.662 0.662
#> SD:hclust 2 0.359 0.667 0.860 0.413 0.576 0.576
#> CV:hclust 2 0.809 0.969 0.973 0.231 0.737 0.737
#> MAD:hclust 2 0.313 0.701 0.853 0.423 0.515 0.515
#> ATC:hclust 2 0.967 0.954 0.979 0.346 0.671 0.671
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.915 0.894 0.957 0.332 0.738 0.526
#> CV:NMF 3 0.920 0.907 0.953 0.305 0.783 0.588
#> MAD:NMF 3 0.954 0.920 0.967 0.324 0.755 0.552
#> ATC:NMF 3 0.619 0.816 0.896 0.337 0.797 0.629
#> SD:skmeans 3 0.999 0.977 0.989 0.313 0.781 0.587
#> CV:skmeans 3 0.814 0.870 0.943 0.316 0.736 0.521
#> MAD:skmeans 3 0.949 0.926 0.972 0.312 0.739 0.526
#> ATC:skmeans 3 0.913 0.911 0.961 0.322 0.777 0.593
#> SD:mclust 3 0.944 0.927 0.971 0.935 0.561 0.404
#> CV:mclust 3 0.634 0.727 0.869 0.310 0.759 0.560
#> MAD:mclust 3 0.893 0.921 0.967 0.964 0.532 0.376
#> ATC:mclust 3 0.877 0.883 0.952 0.571 0.687 0.491
#> SD:kmeans 3 0.924 0.904 0.958 0.367 0.693 0.480
#> CV:kmeans 3 0.864 0.899 0.946 1.009 0.609 0.475
#> MAD:kmeans 3 0.918 0.937 0.968 0.309 0.732 0.527
#> ATC:kmeans 3 0.888 0.935 0.971 0.715 0.644 0.477
#> SD:pam 3 0.879 0.909 0.961 0.303 0.737 0.532
#> CV:pam 3 0.859 0.922 0.962 0.231 0.826 0.677
#> MAD:pam 3 0.827 0.840 0.940 0.315 0.775 0.586
#> ATC:pam 3 0.658 0.833 0.867 0.818 0.646 0.490
#> SD:hclust 3 0.393 0.619 0.803 0.312 0.764 0.625
#> CV:hclust 3 0.795 0.917 0.948 0.355 0.990 0.987
#> MAD:hclust 3 0.334 0.543 0.783 0.321 0.864 0.749
#> ATC:hclust 3 0.475 0.631 0.745 0.644 0.698 0.550
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.944 0.884 0.954 0.064654 0.927 0.794
#> CV:NMF 4 0.936 0.909 0.956 0.093952 0.904 0.731
#> MAD:NMF 4 0.926 0.906 0.961 0.061781 0.927 0.798
#> ATC:NMF 4 0.829 0.835 0.930 0.148669 0.811 0.543
#> SD:skmeans 4 0.817 0.860 0.867 0.093226 0.919 0.769
#> CV:skmeans 4 0.792 0.809 0.893 0.116555 0.857 0.618
#> MAD:skmeans 4 0.812 0.701 0.859 0.099804 0.914 0.756
#> ATC:skmeans 4 0.771 0.810 0.896 0.106309 0.942 0.838
#> SD:mclust 4 0.748 0.781 0.902 0.000941 0.732 0.443
#> CV:mclust 4 0.745 0.805 0.894 0.111055 0.841 0.601
#> MAD:mclust 4 0.904 0.840 0.930 0.013090 0.811 0.571
#> ATC:mclust 4 0.695 0.734 0.862 0.057762 0.885 0.705
#> SD:kmeans 4 0.688 0.740 0.826 0.115203 0.872 0.660
#> CV:kmeans 4 0.661 0.721 0.825 0.152119 0.893 0.732
#> MAD:kmeans 4 0.658 0.762 0.841 0.120642 0.858 0.627
#> ATC:kmeans 4 0.618 0.615 0.752 0.131265 0.901 0.735
#> SD:pam 4 0.733 0.734 0.869 0.154340 0.898 0.717
#> CV:pam 4 0.663 0.698 0.867 0.171178 0.881 0.706
#> MAD:pam 4 0.725 0.697 0.860 0.153303 0.858 0.623
#> ATC:pam 4 0.668 0.787 0.884 0.161773 0.877 0.680
#> SD:hclust 4 0.551 0.669 0.823 0.148208 0.930 0.845
#> CV:hclust 4 0.424 0.652 0.739 0.521751 0.995 0.993
#> MAD:hclust 4 0.474 0.663 0.820 0.138226 0.816 0.621
#> ATC:hclust 4 0.484 0.663 0.801 0.107769 0.819 0.590
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.797 0.773 0.888 0.0751 0.900 0.689
#> CV:NMF 5 0.719 0.645 0.798 0.0673 0.974 0.908
#> MAD:NMF 5 0.795 0.781 0.890 0.0752 0.897 0.687
#> ATC:NMF 5 0.722 0.713 0.854 0.0664 0.844 0.513
#> SD:skmeans 5 0.809 0.697 0.825 0.0621 0.865 0.591
#> CV:skmeans 5 0.764 0.651 0.786 0.0711 0.862 0.552
#> MAD:skmeans 5 0.766 0.729 0.784 0.0632 0.885 0.632
#> ATC:skmeans 5 0.843 0.849 0.902 0.0811 0.917 0.732
#> SD:mclust 5 0.650 0.449 0.768 0.0899 0.838 0.612
#> CV:mclust 5 0.711 0.682 0.771 0.0798 0.807 0.447
#> MAD:mclust 5 0.797 0.787 0.873 0.1099 0.894 0.700
#> ATC:mclust 5 0.632 0.668 0.804 0.0538 0.930 0.789
#> SD:kmeans 5 0.715 0.707 0.797 0.0812 0.929 0.750
#> CV:kmeans 5 0.690 0.638 0.777 0.0815 0.939 0.808
#> MAD:kmeans 5 0.742 0.697 0.802 0.0738 0.936 0.770
#> ATC:kmeans 5 0.589 0.468 0.731 0.0726 0.839 0.518
#> SD:pam 5 0.703 0.576 0.784 0.0488 0.938 0.776
#> CV:pam 5 0.646 0.487 0.723 0.0794 0.896 0.687
#> MAD:pam 5 0.810 0.806 0.911 0.0729 0.860 0.532
#> ATC:pam 5 0.681 0.798 0.858 0.0368 0.972 0.902
#> SD:hclust 5 0.523 0.609 0.780 0.0665 0.979 0.946
#> CV:hclust 5 0.527 0.583 0.796 0.1113 0.795 0.718
#> MAD:hclust 5 0.518 0.623 0.794 0.0775 0.963 0.900
#> ATC:hclust 5 0.539 0.617 0.792 0.0759 0.918 0.768
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.781 0.743 0.862 0.0524 0.938 0.764
#> CV:NMF 6 0.687 0.496 0.689 0.0484 0.910 0.682
#> MAD:NMF 6 0.803 0.753 0.866 0.0539 0.944 0.788
#> ATC:NMF 6 0.775 0.716 0.862 0.0445 0.925 0.680
#> SD:skmeans 6 0.800 0.696 0.823 0.0479 0.924 0.706
#> CV:skmeans 6 0.795 0.735 0.863 0.0488 0.915 0.635
#> MAD:skmeans 6 0.808 0.723 0.834 0.0408 0.974 0.888
#> ATC:skmeans 6 0.853 0.825 0.895 0.0373 0.957 0.823
#> SD:mclust 6 0.723 0.705 0.775 0.1088 0.843 0.543
#> CV:mclust 6 0.771 0.783 0.865 0.0645 0.934 0.702
#> MAD:mclust 6 0.735 0.737 0.812 0.0787 0.889 0.603
#> ATC:mclust 6 0.660 0.605 0.778 0.0675 0.917 0.722
#> SD:kmeans 6 0.737 0.650 0.773 0.0475 0.935 0.732
#> CV:kmeans 6 0.715 0.560 0.748 0.0551 0.893 0.629
#> MAD:kmeans 6 0.734 0.679 0.766 0.0436 0.929 0.714
#> ATC:kmeans 6 0.653 0.562 0.710 0.0515 0.872 0.528
#> SD:pam 6 0.822 0.772 0.893 0.0531 0.874 0.527
#> CV:pam 6 0.682 0.675 0.739 0.0456 0.839 0.469
#> MAD:pam 6 0.821 0.792 0.903 0.0250 0.977 0.888
#> ATC:pam 6 0.771 0.779 0.863 0.0397 0.960 0.853
#> SD:hclust 6 0.537 0.570 0.778 0.0664 0.869 0.678
#> CV:hclust 6 0.526 0.563 0.776 0.0341 0.950 0.906
#> MAD:hclust 6 0.563 0.488 0.734 0.0723 0.942 0.831
#> ATC:hclust 6 0.567 0.651 0.814 0.0440 0.975 0.918
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.359 0.667 0.860 0.4128 0.576 0.576
#> 3 3 0.393 0.619 0.803 0.3117 0.764 0.625
#> 4 4 0.551 0.669 0.823 0.1482 0.930 0.845
#> 5 5 0.523 0.609 0.780 0.0665 0.979 0.946
#> 6 6 0.537 0.570 0.778 0.0664 0.869 0.678
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.8246 1.000 0.000
#> SRR1349562 1 0.0000 0.8246 1.000 0.000
#> SRR1353376 2 0.8207 0.6206 0.256 0.744
#> SRR1499040 1 0.0376 0.8247 0.996 0.004
#> SRR1322312 1 0.0000 0.8246 1.000 0.000
#> SRR1324412 1 0.7219 0.7288 0.800 0.200
#> SRR1100991 1 0.7219 0.7288 0.800 0.200
#> SRR1349479 2 0.8207 0.6206 0.256 0.744
#> SRR1431248 1 0.4161 0.8055 0.916 0.084
#> SRR1405054 1 0.7139 0.7324 0.804 0.196
#> SRR1312266 1 0.0000 0.8246 1.000 0.000
#> SRR1409790 1 0.7219 0.7288 0.800 0.200
#> SRR1352507 1 0.7219 0.7288 0.800 0.200
#> SRR1383763 1 0.0000 0.8246 1.000 0.000
#> SRR1468314 2 0.1184 0.8014 0.016 0.984
#> SRR1473674 2 0.1184 0.8024 0.016 0.984
#> SRR1390499 1 0.0000 0.8246 1.000 0.000
#> SRR821043 2 0.0000 0.7950 0.000 1.000
#> SRR1455653 2 0.0000 0.7950 0.000 1.000
#> SRR1335236 2 0.1184 0.8024 0.016 0.984
#> SRR1095383 2 0.0672 0.7996 0.008 0.992
#> SRR1479489 1 0.0672 0.8257 0.992 0.008
#> SRR1310433 2 0.1184 0.8024 0.016 0.984
#> SRR1073435 2 0.9970 0.0972 0.468 0.532
#> SRR659649 1 0.9998 0.0530 0.508 0.492
#> SRR1395999 1 0.0000 0.8246 1.000 0.000
#> SRR1105248 2 0.8144 0.6250 0.252 0.748
#> SRR1338257 1 0.0672 0.8258 0.992 0.008
#> SRR1499395 1 0.9000 0.5538 0.684 0.316
#> SRR1350002 2 0.1184 0.8024 0.016 0.984
#> SRR1489757 1 0.7219 0.7288 0.800 0.200
#> SRR1414637 1 0.6048 0.7785 0.852 0.148
#> SRR1478113 2 0.0938 0.7996 0.012 0.988
#> SRR1322477 1 0.2603 0.8221 0.956 0.044
#> SRR1478789 1 1.0000 0.0372 0.504 0.496
#> SRR1414185 1 1.0000 0.0372 0.504 0.496
#> SRR1069141 2 0.1184 0.8024 0.016 0.984
#> SRR1376852 1 0.0000 0.8246 1.000 0.000
#> SRR1323491 1 0.0000 0.8246 1.000 0.000
#> SRR1338103 1 0.5178 0.7829 0.884 0.116
#> SRR1472012 1 0.3274 0.8189 0.940 0.060
#> SRR1340325 1 0.0938 0.8259 0.988 0.012
#> SRR1087321 1 1.0000 0.0372 0.504 0.496
#> SRR1488790 1 0.0000 0.8246 1.000 0.000
#> SRR1334866 1 0.5629 0.7877 0.868 0.132
#> SRR1089446 1 0.7376 0.7208 0.792 0.208
#> SRR1344445 1 0.7299 0.7253 0.796 0.204
#> SRR1412969 1 1.0000 0.0372 0.504 0.496
#> SRR1071668 1 0.7219 0.7288 0.800 0.200
#> SRR1075804 1 0.1414 0.8254 0.980 0.020
#> SRR1383283 2 0.9970 0.0972 0.468 0.532
#> SRR1350239 2 0.8207 0.6216 0.256 0.744
#> SRR1353878 1 0.0376 0.8251 0.996 0.004
#> SRR1375721 1 0.0000 0.8246 1.000 0.000
#> SRR1083983 1 0.4298 0.8083 0.912 0.088
#> SRR1090095 1 0.0000 0.8246 1.000 0.000
#> SRR1414792 1 0.0000 0.8246 1.000 0.000
#> SRR1075102 2 0.0938 0.7996 0.012 0.988
#> SRR1098737 1 0.1414 0.8254 0.980 0.020
#> SRR1349409 1 0.0000 0.8246 1.000 0.000
#> SRR1413008 2 0.8207 0.6216 0.256 0.744
#> SRR1407179 1 0.7745 0.7002 0.772 0.228
#> SRR1095913 2 0.9963 0.1114 0.464 0.536
#> SRR1403544 1 0.0000 0.8246 1.000 0.000
#> SRR1490546 1 0.0000 0.8246 1.000 0.000
#> SRR807971 1 0.7299 0.7253 0.796 0.204
#> SRR1436228 1 0.8661 0.6109 0.712 0.288
#> SRR1445218 2 0.1184 0.8024 0.016 0.984
#> SRR1485438 2 0.2236 0.7937 0.036 0.964
#> SRR1358143 1 0.0000 0.8246 1.000 0.000
#> SRR1328760 1 0.0376 0.8251 0.996 0.004
#> SRR1380806 1 0.0000 0.8246 1.000 0.000
#> SRR1379426 1 1.0000 0.0372 0.504 0.496
#> SRR1087007 1 1.0000 0.0372 0.504 0.496
#> SRR1086256 1 0.6048 0.7785 0.852 0.148
#> SRR1346734 2 0.0000 0.7950 0.000 1.000
#> SRR1414515 1 0.0000 0.8246 1.000 0.000
#> SRR1082151 1 0.4939 0.8008 0.892 0.108
#> SRR1349320 2 0.0938 0.7996 0.012 0.988
#> SRR1317554 2 0.0000 0.7950 0.000 1.000
#> SRR1076022 2 0.1184 0.8024 0.016 0.984
#> SRR1339573 1 0.8813 0.5869 0.700 0.300
#> SRR1455878 1 0.1414 0.8258 0.980 0.020
#> SRR1446203 1 1.0000 0.0375 0.504 0.496
#> SRR1387397 1 0.3733 0.8169 0.928 0.072
#> SRR1402590 1 0.0000 0.8246 1.000 0.000
#> SRR1317532 1 0.1184 0.8256 0.984 0.016
#> SRR1331488 1 0.5294 0.7888 0.880 0.120
#> SRR1499675 1 0.5178 0.7829 0.884 0.116
#> SRR1440467 2 0.9815 0.2642 0.420 0.580
#> SRR807995 2 0.1184 0.8024 0.016 0.984
#> SRR1476485 2 0.0000 0.7950 0.000 1.000
#> SRR1388214 1 0.1843 0.8253 0.972 0.028
#> SRR1456051 1 0.0000 0.8246 1.000 0.000
#> SRR1473275 1 0.8763 0.5924 0.704 0.296
#> SRR1444083 1 0.0672 0.8258 0.992 0.008
#> SRR1313807 2 0.9970 0.0972 0.468 0.532
#> SRR1470751 1 0.4939 0.8008 0.892 0.108
#> SRR1403434 2 0.9815 0.2642 0.420 0.580
#> SRR1390540 1 0.0000 0.8246 1.000 0.000
#> SRR1093861 2 0.1184 0.8024 0.016 0.984
#> SRR1325290 1 0.3431 0.8176 0.936 0.064
#> SRR1070689 1 0.0000 0.8246 1.000 0.000
#> SRR1384049 1 0.0000 0.8246 1.000 0.000
#> SRR1081184 1 0.0000 0.8246 1.000 0.000
#> SRR1324295 1 0.0000 0.8246 1.000 0.000
#> SRR1365313 1 0.8813 0.5871 0.700 0.300
#> SRR1321877 1 1.0000 0.0372 0.504 0.496
#> SRR815711 1 0.7299 0.7247 0.796 0.204
#> SRR1433476 2 0.8207 0.6206 0.256 0.744
#> SRR1101883 1 0.7299 0.7253 0.796 0.204
#> SRR1433729 2 0.9754 0.2889 0.408 0.592
#> SRR1341877 1 0.5178 0.7829 0.884 0.116
#> SRR1090556 1 0.5946 0.7723 0.856 0.144
#> SRR1357389 1 0.7219 0.7288 0.800 0.200
#> SRR1404227 2 0.9977 0.0798 0.472 0.528
#> SRR1376830 1 0.0000 0.8246 1.000 0.000
#> SRR1500661 1 0.0000 0.8246 1.000 0.000
#> SRR1080294 2 0.0672 0.7996 0.008 0.992
#> SRR1336314 2 0.0000 0.7950 0.000 1.000
#> SRR1102152 1 0.2043 0.8249 0.968 0.032
#> SRR1345244 1 1.0000 0.0372 0.504 0.496
#> SRR1478637 1 0.3431 0.8069 0.936 0.064
#> SRR1443776 1 1.0000 0.0372 0.504 0.496
#> SRR1120939 1 1.0000 0.0375 0.504 0.496
#> SRR1080117 1 1.0000 0.0372 0.504 0.496
#> SRR1102899 2 0.1184 0.8024 0.016 0.984
#> SRR1091865 1 0.1414 0.8261 0.980 0.020
#> SRR1361072 1 0.0000 0.8246 1.000 0.000
#> SRR1487890 1 0.0000 0.8246 1.000 0.000
#> SRR1349456 2 0.9977 0.0798 0.472 0.528
#> SRR1389384 1 0.4939 0.8008 0.892 0.108
#> SRR1316096 2 0.1184 0.8024 0.016 0.984
#> SRR1408512 1 0.2423 0.8237 0.960 0.040
#> SRR1447547 2 0.8207 0.6216 0.256 0.744
#> SRR1354053 2 0.0000 0.7950 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1353376 3 0.5497 0.4114 0.000 0.292 0.708
#> SRR1499040 1 0.3879 0.7686 0.848 0.000 0.152
#> SRR1322312 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1324412 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1100991 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1349479 3 0.5497 0.4114 0.000 0.292 0.708
#> SRR1431248 1 0.5254 0.6800 0.736 0.000 0.264
#> SRR1405054 1 0.5560 0.5921 0.700 0.000 0.300
#> SRR1312266 1 0.0592 0.8140 0.988 0.000 0.012
#> SRR1409790 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1352507 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1383763 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1468314 3 0.5882 0.0926 0.000 0.348 0.652
#> SRR1473674 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1390499 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.9060 0.000 1.000 0.000
#> SRR1455653 2 0.2878 0.8665 0.000 0.904 0.096
#> SRR1335236 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1095383 2 0.5497 0.5979 0.000 0.708 0.292
#> SRR1479489 1 0.1643 0.8140 0.956 0.000 0.044
#> SRR1310433 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1073435 3 0.5643 0.6426 0.220 0.020 0.760
#> SRR659649 3 0.5058 0.6195 0.244 0.000 0.756
#> SRR1395999 1 0.0892 0.8149 0.980 0.000 0.020
#> SRR1105248 3 0.7164 0.1344 0.024 0.452 0.524
#> SRR1338257 1 0.1163 0.8149 0.972 0.000 0.028
#> SRR1499395 3 0.6225 0.1395 0.432 0.000 0.568
#> SRR1350002 3 0.5254 0.2324 0.000 0.264 0.736
#> SRR1489757 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1414637 1 0.5810 0.5952 0.664 0.000 0.336
#> SRR1478113 2 0.1529 0.8971 0.000 0.960 0.040
#> SRR1322477 1 0.4291 0.7537 0.820 0.000 0.180
#> SRR1478789 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1414185 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1069141 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1376852 1 0.3038 0.7947 0.896 0.000 0.104
#> SRR1323491 1 0.0237 0.8130 0.996 0.000 0.004
#> SRR1338103 1 0.5621 0.6215 0.692 0.000 0.308
#> SRR1472012 1 0.4887 0.7225 0.772 0.000 0.228
#> SRR1340325 1 0.1860 0.8124 0.948 0.000 0.052
#> SRR1087321 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1488790 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1334866 1 0.5678 0.6230 0.684 0.000 0.316
#> SRR1089446 1 0.6126 0.4339 0.600 0.000 0.400
#> SRR1344445 1 0.6079 0.4625 0.612 0.000 0.388
#> SRR1412969 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1071668 1 0.5988 0.4988 0.632 0.000 0.368
#> SRR1075804 1 0.1860 0.8115 0.948 0.000 0.052
#> SRR1383283 3 0.5643 0.6426 0.220 0.020 0.760
#> SRR1350239 3 0.7278 0.1250 0.028 0.456 0.516
#> SRR1353878 1 0.0892 0.8147 0.980 0.000 0.020
#> SRR1375721 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1083983 1 0.5178 0.6955 0.744 0.000 0.256
#> SRR1090095 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1075102 2 0.1529 0.8971 0.000 0.960 0.040
#> SRR1098737 1 0.1860 0.8115 0.948 0.000 0.052
#> SRR1349409 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1413008 3 0.7278 0.1250 0.028 0.456 0.516
#> SRR1407179 1 0.6215 0.3919 0.572 0.000 0.428
#> SRR1095913 3 0.5772 0.6429 0.220 0.024 0.756
#> SRR1403544 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1490546 1 0.0424 0.8136 0.992 0.000 0.008
#> SRR807971 1 0.6079 0.4625 0.612 0.000 0.388
#> SRR1436228 1 0.6521 0.1587 0.504 0.004 0.492
#> SRR1445218 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1485438 3 0.5285 0.2596 0.004 0.244 0.752
#> SRR1358143 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1328760 1 0.0892 0.8147 0.980 0.000 0.020
#> SRR1380806 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1379426 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1087007 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1086256 1 0.5810 0.5952 0.664 0.000 0.336
#> SRR1346734 2 0.0000 0.9060 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1082151 1 0.5178 0.6950 0.744 0.000 0.256
#> SRR1349320 2 0.1529 0.8971 0.000 0.960 0.040
#> SRR1317554 2 0.0000 0.9060 0.000 1.000 0.000
#> SRR1076022 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1339573 3 0.6252 0.0757 0.444 0.000 0.556
#> SRR1455878 1 0.2711 0.8028 0.912 0.000 0.088
#> SRR1446203 3 0.5285 0.6224 0.244 0.004 0.752
#> SRR1387397 1 0.4842 0.7293 0.776 0.000 0.224
#> SRR1402590 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1317532 1 0.1411 0.8142 0.964 0.000 0.036
#> SRR1331488 1 0.4526 0.7501 0.856 0.040 0.104
#> SRR1499675 1 0.5650 0.6144 0.688 0.000 0.312
#> SRR1440467 3 0.6431 0.6472 0.156 0.084 0.760
#> SRR807995 3 0.5254 0.2324 0.000 0.264 0.736
#> SRR1476485 2 0.0000 0.9060 0.000 1.000 0.000
#> SRR1388214 1 0.2066 0.8102 0.940 0.000 0.060
#> SRR1456051 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1473275 3 0.6274 0.0384 0.456 0.000 0.544
#> SRR1444083 1 0.1163 0.8149 0.972 0.000 0.028
#> SRR1313807 3 0.5643 0.6426 0.220 0.020 0.760
#> SRR1470751 1 0.5178 0.6950 0.744 0.000 0.256
#> SRR1403434 3 0.6431 0.6472 0.156 0.084 0.760
#> SRR1390540 1 0.0237 0.8130 0.996 0.000 0.004
#> SRR1093861 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1325290 1 0.4931 0.7191 0.768 0.000 0.232
#> SRR1070689 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1365313 3 0.6513 -0.0757 0.476 0.004 0.520
#> SRR1321877 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR815711 1 0.6026 0.4813 0.624 0.000 0.376
#> SRR1433476 3 0.5497 0.4114 0.000 0.292 0.708
#> SRR1101883 1 0.6079 0.4625 0.612 0.000 0.388
#> SRR1433729 3 0.6562 0.6560 0.184 0.072 0.744
#> SRR1341877 1 0.5621 0.6215 0.692 0.000 0.308
#> SRR1090556 1 0.5810 0.5869 0.664 0.000 0.336
#> SRR1357389 1 0.6008 0.4906 0.628 0.000 0.372
#> SRR1404227 3 0.5595 0.6368 0.228 0.016 0.756
#> SRR1376830 1 0.0237 0.8130 0.996 0.000 0.004
#> SRR1500661 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1080294 2 0.5497 0.5979 0.000 0.708 0.292
#> SRR1336314 2 0.0000 0.9060 0.000 1.000 0.000
#> SRR1102152 1 0.2066 0.8098 0.940 0.000 0.060
#> SRR1345244 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1478637 1 0.5098 0.6912 0.752 0.000 0.248
#> SRR1443776 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1120939 3 0.5285 0.6224 0.244 0.004 0.752
#> SRR1080117 3 0.5016 0.6248 0.240 0.000 0.760
#> SRR1102899 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1091865 1 0.1529 0.8141 0.960 0.000 0.040
#> SRR1361072 1 0.0424 0.8136 0.992 0.000 0.008
#> SRR1487890 1 0.0000 0.8121 1.000 0.000 0.000
#> SRR1349456 3 0.5551 0.6399 0.224 0.016 0.760
#> SRR1389384 1 0.5178 0.6950 0.744 0.000 0.256
#> SRR1316096 3 0.5397 0.2087 0.000 0.280 0.720
#> SRR1408512 1 0.2959 0.7980 0.900 0.000 0.100
#> SRR1447547 3 0.7278 0.1250 0.028 0.456 0.516
#> SRR1354053 2 0.2878 0.8665 0.000 0.904 0.096
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1349562 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1353376 3 0.4560 0.5208 0.004 0.000 0.700 0.296
#> SRR1499040 1 0.4697 0.6314 0.696 0.000 0.296 0.008
#> SRR1322312 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1324412 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1100991 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1349479 3 0.4560 0.5208 0.004 0.000 0.700 0.296
#> SRR1431248 1 0.5182 0.5104 0.632 0.004 0.356 0.008
#> SRR1405054 1 0.4800 0.5198 0.656 0.000 0.340 0.004
#> SRR1312266 1 0.0921 0.7775 0.972 0.000 0.028 0.000
#> SRR1409790 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1352507 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1383763 1 0.2602 0.7154 0.908 0.008 0.076 0.008
#> SRR1468314 2 0.3307 0.8649 0.000 0.868 0.028 0.104
#> SRR1473674 2 0.1297 0.9719 0.000 0.964 0.020 0.016
#> SRR1390499 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR821043 4 0.0921 0.8442 0.000 0.028 0.000 0.972
#> SRR1455653 4 0.4428 0.6670 0.000 0.276 0.004 0.720
#> SRR1335236 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1095383 4 0.5163 0.2562 0.000 0.480 0.004 0.516
#> SRR1479489 1 0.1978 0.7745 0.928 0.000 0.068 0.004
#> SRR1310433 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1073435 3 0.3436 0.7938 0.080 0.036 0.876 0.008
#> SRR659649 3 0.2466 0.8010 0.096 0.000 0.900 0.004
#> SRR1395999 1 0.0817 0.7784 0.976 0.000 0.024 0.000
#> SRR1105248 3 0.6205 0.2090 0.020 0.020 0.500 0.460
#> SRR1338257 1 0.1389 0.7764 0.952 0.000 0.048 0.000
#> SRR1499395 3 0.4584 0.4916 0.300 0.000 0.696 0.004
#> SRR1350002 2 0.1118 0.9446 0.000 0.964 0.036 0.000
#> SRR1489757 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1414637 1 0.6450 0.4038 0.552 0.048 0.388 0.012
#> SRR1478113 4 0.1584 0.8392 0.000 0.036 0.012 0.952
#> SRR1322477 1 0.4785 0.6334 0.720 0.012 0.264 0.004
#> SRR1478789 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1414185 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1069141 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1376852 1 0.3448 0.7234 0.828 0.000 0.168 0.004
#> SRR1323491 1 0.0524 0.7768 0.988 0.000 0.008 0.004
#> SRR1338103 1 0.4898 0.4114 0.584 0.000 0.416 0.000
#> SRR1472012 1 0.4917 0.5629 0.656 0.000 0.336 0.008
#> SRR1340325 1 0.1978 0.7736 0.928 0.000 0.068 0.004
#> SRR1087321 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1488790 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1334866 1 0.5954 0.4309 0.572 0.028 0.392 0.008
#> SRR1089446 1 0.5158 0.2472 0.524 0.000 0.472 0.004
#> SRR1344445 1 0.5126 0.3313 0.552 0.000 0.444 0.004
#> SRR1412969 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1071668 1 0.5088 0.3791 0.572 0.000 0.424 0.004
#> SRR1075804 1 0.1474 0.7749 0.948 0.000 0.052 0.000
#> SRR1383283 3 0.3436 0.7938 0.080 0.036 0.876 0.008
#> SRR1350239 3 0.6383 0.1908 0.028 0.020 0.484 0.468
#> SRR1353878 1 0.1302 0.7768 0.956 0.000 0.044 0.000
#> SRR1375721 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1083983 1 0.5198 0.5231 0.628 0.004 0.360 0.008
#> SRR1090095 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1414792 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1075102 4 0.1584 0.8392 0.000 0.036 0.012 0.952
#> SRR1098737 1 0.1474 0.7749 0.948 0.000 0.052 0.000
#> SRR1349409 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1413008 3 0.6383 0.1908 0.028 0.020 0.484 0.468
#> SRR1407179 3 0.4977 -0.0788 0.460 0.000 0.540 0.000
#> SRR1095913 3 0.3526 0.7917 0.080 0.040 0.872 0.008
#> SRR1403544 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1490546 1 0.0657 0.7772 0.984 0.000 0.012 0.004
#> SRR807971 1 0.5126 0.3313 0.552 0.000 0.444 0.004
#> SRR1436228 3 0.5004 0.2111 0.392 0.004 0.604 0.000
#> SRR1445218 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1485438 2 0.1822 0.9199 0.004 0.944 0.044 0.008
#> SRR1358143 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1328760 1 0.1302 0.7768 0.956 0.000 0.044 0.000
#> SRR1380806 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1379426 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1087007 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1086256 1 0.6450 0.4038 0.552 0.048 0.388 0.012
#> SRR1346734 4 0.0817 0.8445 0.000 0.024 0.000 0.976
#> SRR1414515 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1082151 1 0.5868 0.5588 0.644 0.040 0.308 0.008
#> SRR1349320 4 0.1584 0.8392 0.000 0.036 0.012 0.952
#> SRR1317554 4 0.1389 0.8418 0.000 0.048 0.000 0.952
#> SRR1076022 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1339573 3 0.4655 0.4562 0.312 0.000 0.684 0.004
#> SRR1455878 1 0.2973 0.7406 0.856 0.000 0.144 0.000
#> SRR1446203 3 0.2651 0.8023 0.096 0.004 0.896 0.004
#> SRR1387397 1 0.4356 0.6299 0.708 0.000 0.292 0.000
#> SRR1402590 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1317532 1 0.1118 0.7778 0.964 0.000 0.036 0.000
#> SRR1331488 1 0.3942 0.7176 0.852 0.012 0.092 0.044
#> SRR1499675 1 0.4916 0.3938 0.576 0.000 0.424 0.000
#> SRR1440467 3 0.2522 0.7467 0.016 0.000 0.908 0.076
#> SRR807995 2 0.1118 0.9446 0.000 0.964 0.036 0.000
#> SRR1476485 4 0.0817 0.8445 0.000 0.024 0.000 0.976
#> SRR1388214 1 0.2081 0.7675 0.916 0.000 0.084 0.000
#> SRR1456051 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1473275 3 0.4720 0.4291 0.324 0.000 0.672 0.004
#> SRR1444083 1 0.1389 0.7764 0.952 0.000 0.048 0.000
#> SRR1313807 3 0.3436 0.7938 0.080 0.036 0.876 0.008
#> SRR1470751 1 0.5868 0.5588 0.644 0.040 0.308 0.008
#> SRR1403434 3 0.2522 0.7467 0.016 0.000 0.908 0.076
#> SRR1390540 1 0.0524 0.7768 0.988 0.000 0.008 0.004
#> SRR1093861 2 0.1297 0.9719 0.000 0.964 0.020 0.016
#> SRR1325290 1 0.4819 0.5558 0.652 0.000 0.344 0.004
#> SRR1070689 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1384049 1 0.2602 0.7154 0.908 0.008 0.076 0.008
#> SRR1081184 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1324295 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1365313 3 0.5024 0.3140 0.360 0.008 0.632 0.000
#> SRR1321877 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR815711 1 0.5132 0.3156 0.548 0.000 0.448 0.004
#> SRR1433476 3 0.4560 0.5208 0.004 0.000 0.700 0.296
#> SRR1101883 1 0.5126 0.3313 0.552 0.000 0.444 0.004
#> SRR1433729 3 0.4020 0.7569 0.044 0.056 0.860 0.040
#> SRR1341877 1 0.4898 0.4114 0.584 0.000 0.416 0.000
#> SRR1090556 1 0.4955 0.3560 0.556 0.000 0.444 0.000
#> SRR1357389 1 0.5097 0.3689 0.568 0.000 0.428 0.004
#> SRR1404227 3 0.3360 0.7947 0.084 0.036 0.876 0.004
#> SRR1376830 1 0.0524 0.7768 0.988 0.000 0.008 0.004
#> SRR1500661 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1080294 4 0.5163 0.2562 0.000 0.480 0.004 0.516
#> SRR1336314 4 0.0817 0.8445 0.000 0.024 0.000 0.976
#> SRR1102152 1 0.2149 0.7665 0.912 0.000 0.088 0.000
#> SRR1345244 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1478637 1 0.5299 0.4894 0.600 0.004 0.388 0.008
#> SRR1443776 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1120939 3 0.2651 0.8023 0.096 0.004 0.896 0.004
#> SRR1080117 3 0.2216 0.8050 0.092 0.000 0.908 0.000
#> SRR1102899 2 0.1411 0.9726 0.000 0.960 0.020 0.020
#> SRR1091865 1 0.1807 0.7755 0.940 0.008 0.052 0.000
#> SRR1361072 1 0.0657 0.7772 0.984 0.000 0.012 0.004
#> SRR1487890 1 0.0188 0.7753 0.996 0.000 0.000 0.004
#> SRR1349456 3 0.3292 0.7943 0.080 0.036 0.880 0.004
#> SRR1389384 1 0.5868 0.5588 0.644 0.040 0.308 0.008
#> SRR1316096 2 0.1297 0.9719 0.000 0.964 0.020 0.016
#> SRR1408512 1 0.2704 0.7516 0.876 0.000 0.124 0.000
#> SRR1447547 3 0.6383 0.1908 0.028 0.020 0.484 0.468
#> SRR1354053 4 0.4456 0.6638 0.000 0.280 0.004 0.716
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.1341 0.6557 0.944 0.000 0.000 0.000 0.056
#> SRR1349562 1 0.2127 0.6277 0.892 0.000 0.000 0.000 0.108
#> SRR1353376 3 0.5379 0.4616 0.004 0.000 0.672 0.208 0.116
#> SRR1499040 1 0.6349 0.3947 0.524 0.000 0.232 0.000 0.244
#> SRR1322312 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1324412 1 0.5402 0.3350 0.528 0.000 0.420 0.004 0.048
#> SRR1100991 1 0.5402 0.3350 0.528 0.000 0.420 0.004 0.048
#> SRR1349479 3 0.5379 0.4616 0.004 0.000 0.672 0.208 0.116
#> SRR1431248 1 0.5237 0.5104 0.628 0.000 0.300 0.000 0.072
#> SRR1405054 1 0.5107 0.4753 0.620 0.000 0.332 0.004 0.044
#> SRR1312266 1 0.1168 0.6791 0.960 0.000 0.008 0.000 0.032
#> SRR1409790 1 0.5402 0.3350 0.528 0.000 0.420 0.004 0.048
#> SRR1352507 1 0.5402 0.3350 0.528 0.000 0.420 0.004 0.048
#> SRR1383763 5 0.3852 1.0000 0.220 0.000 0.020 0.000 0.760
#> SRR1468314 2 0.2460 0.8294 0.000 0.900 0.024 0.072 0.004
#> SRR1473674 2 0.0486 0.8929 0.000 0.988 0.004 0.004 0.004
#> SRR1390499 1 0.1121 0.6604 0.956 0.000 0.000 0.000 0.044
#> SRR821043 4 0.1074 0.8904 0.000 0.016 0.012 0.968 0.004
#> SRR1455653 4 0.4283 0.6014 0.000 0.292 0.012 0.692 0.004
#> SRR1335236 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1095383 2 0.4803 -0.0565 0.000 0.496 0.012 0.488 0.004
#> SRR1479489 1 0.2300 0.6865 0.908 0.000 0.052 0.000 0.040
#> SRR1310433 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1073435 3 0.3766 0.7576 0.080 0.032 0.844 0.004 0.040
#> SRR659649 3 0.1864 0.7742 0.068 0.000 0.924 0.004 0.004
#> SRR1395999 1 0.0579 0.6782 0.984 0.000 0.008 0.000 0.008
#> SRR1105248 3 0.6198 0.1648 0.020 0.000 0.476 0.424 0.080
#> SRR1338257 1 0.1872 0.6821 0.928 0.000 0.020 0.000 0.052
#> SRR1499395 3 0.4219 0.5041 0.264 0.000 0.716 0.004 0.016
#> SRR1350002 2 0.1484 0.8664 0.000 0.944 0.008 0.000 0.048
#> SRR1489757 1 0.5402 0.3350 0.528 0.000 0.420 0.004 0.048
#> SRR1414637 1 0.6175 0.4066 0.536 0.008 0.336 0.000 0.120
#> SRR1478113 4 0.1205 0.8893 0.000 0.004 0.000 0.956 0.040
#> SRR1322477 1 0.4919 0.6086 0.700 0.004 0.228 0.000 0.068
#> SRR1478789 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1414185 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1069141 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1376852 1 0.3608 0.6603 0.812 0.000 0.148 0.000 0.040
#> SRR1323491 1 0.0794 0.6699 0.972 0.000 0.000 0.000 0.028
#> SRR1338103 1 0.5260 0.4367 0.592 0.000 0.348 0.000 0.060
#> SRR1472012 1 0.5182 0.5383 0.632 0.000 0.300 0.000 0.068
#> SRR1340325 1 0.2278 0.6879 0.908 0.000 0.060 0.000 0.032
#> SRR1087321 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1488790 1 0.1341 0.6563 0.944 0.000 0.000 0.000 0.056
#> SRR1334866 1 0.5855 0.4228 0.552 0.004 0.348 0.000 0.096
#> SRR1089446 1 0.5319 0.2202 0.492 0.000 0.464 0.004 0.040
#> SRR1344445 1 0.5303 0.2985 0.516 0.000 0.440 0.004 0.040
#> SRR1412969 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1071668 1 0.5396 0.3421 0.532 0.000 0.416 0.004 0.048
#> SRR1075804 1 0.1893 0.6877 0.928 0.000 0.048 0.000 0.024
#> SRR1383283 3 0.3766 0.7576 0.080 0.032 0.844 0.004 0.040
#> SRR1350239 3 0.6391 0.1431 0.028 0.000 0.460 0.428 0.084
#> SRR1353878 1 0.1740 0.6812 0.932 0.000 0.012 0.000 0.056
#> SRR1375721 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1083983 1 0.5338 0.5010 0.604 0.000 0.324 0.000 0.072
#> SRR1090095 1 0.1851 0.6399 0.912 0.000 0.000 0.000 0.088
#> SRR1414792 1 0.1851 0.6399 0.912 0.000 0.000 0.000 0.088
#> SRR1075102 4 0.1205 0.8893 0.000 0.004 0.000 0.956 0.040
#> SRR1098737 1 0.1893 0.6877 0.928 0.000 0.048 0.000 0.024
#> SRR1349409 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1413008 3 0.6391 0.1431 0.028 0.000 0.460 0.428 0.084
#> SRR1407179 3 0.5557 -0.1449 0.460 0.000 0.472 0.000 0.068
#> SRR1095913 3 0.3846 0.7555 0.080 0.036 0.840 0.004 0.040
#> SRR1403544 1 0.1792 0.6377 0.916 0.000 0.000 0.000 0.084
#> SRR1490546 1 0.0609 0.6693 0.980 0.000 0.000 0.000 0.020
#> SRR807971 1 0.5303 0.2985 0.516 0.000 0.440 0.004 0.040
#> SRR1436228 3 0.5542 0.1102 0.396 0.000 0.532 0.000 0.072
#> SRR1445218 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1485438 2 0.2005 0.8473 0.004 0.924 0.016 0.000 0.056
#> SRR1358143 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1328760 1 0.1740 0.6812 0.932 0.000 0.012 0.000 0.056
#> SRR1380806 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1379426 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1087007 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1086256 1 0.6175 0.4066 0.536 0.008 0.336 0.000 0.120
#> SRR1346734 4 0.0162 0.8962 0.000 0.004 0.000 0.996 0.000
#> SRR1414515 1 0.2127 0.6283 0.892 0.000 0.000 0.000 0.108
#> SRR1082151 1 0.5665 0.5386 0.620 0.004 0.268 0.000 0.108
#> SRR1349320 4 0.1205 0.8893 0.000 0.004 0.000 0.956 0.040
#> SRR1317554 4 0.1682 0.8820 0.000 0.044 0.012 0.940 0.004
#> SRR1076022 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1339573 3 0.4291 0.4704 0.276 0.000 0.704 0.004 0.016
#> SRR1455878 1 0.3134 0.6749 0.848 0.000 0.120 0.000 0.032
#> SRR1446203 3 0.2024 0.7748 0.068 0.004 0.920 0.004 0.004
#> SRR1387397 1 0.4755 0.6105 0.696 0.000 0.244 0.000 0.060
#> SRR1402590 1 0.1341 0.6557 0.944 0.000 0.000 0.000 0.056
#> SRR1317532 1 0.1579 0.6844 0.944 0.000 0.032 0.000 0.024
#> SRR1331488 1 0.4409 0.6005 0.800 0.000 0.092 0.040 0.068
#> SRR1499675 1 0.5143 0.4202 0.584 0.000 0.368 0.000 0.048
#> SRR1440467 3 0.1671 0.6717 0.000 0.000 0.924 0.000 0.076
#> SRR807995 2 0.1484 0.8664 0.000 0.944 0.008 0.000 0.048
#> SRR1476485 4 0.0162 0.8962 0.000 0.004 0.000 0.996 0.000
#> SRR1388214 1 0.2592 0.6832 0.892 0.000 0.052 0.000 0.056
#> SRR1456051 1 0.1121 0.6604 0.956 0.000 0.000 0.000 0.044
#> SRR1473275 3 0.4513 0.4437 0.284 0.000 0.688 0.004 0.024
#> SRR1444083 1 0.1872 0.6821 0.928 0.000 0.020 0.000 0.052
#> SRR1313807 3 0.3766 0.7576 0.080 0.032 0.844 0.004 0.040
#> SRR1470751 1 0.5665 0.5386 0.620 0.004 0.268 0.000 0.108
#> SRR1403434 3 0.1608 0.6755 0.000 0.000 0.928 0.000 0.072
#> SRR1390540 1 0.0794 0.6699 0.972 0.000 0.000 0.000 0.028
#> SRR1093861 2 0.0486 0.8929 0.000 0.988 0.004 0.004 0.004
#> SRR1325290 1 0.5325 0.5219 0.616 0.000 0.308 0.000 0.076
#> SRR1070689 1 0.2179 0.6242 0.888 0.000 0.000 0.000 0.112
#> SRR1384049 5 0.3852 1.0000 0.220 0.000 0.020 0.000 0.760
#> SRR1081184 1 0.2127 0.6277 0.892 0.000 0.000 0.000 0.108
#> SRR1324295 1 0.1792 0.6377 0.916 0.000 0.000 0.000 0.084
#> SRR1365313 3 0.5292 0.2187 0.368 0.004 0.580 0.000 0.048
#> SRR1321877 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR815711 1 0.5425 0.2749 0.508 0.000 0.440 0.004 0.048
#> SRR1433476 3 0.5379 0.4616 0.004 0.000 0.672 0.208 0.116
#> SRR1101883 1 0.5303 0.2985 0.516 0.000 0.440 0.004 0.040
#> SRR1433729 3 0.3894 0.7239 0.044 0.052 0.844 0.008 0.052
#> SRR1341877 1 0.5260 0.4367 0.592 0.000 0.348 0.000 0.060
#> SRR1090556 1 0.5449 0.3820 0.556 0.000 0.376 0.000 0.068
#> SRR1357389 1 0.5407 0.3275 0.524 0.000 0.424 0.004 0.048
#> SRR1404227 3 0.3667 0.7571 0.084 0.032 0.844 0.000 0.040
#> SRR1376830 1 0.0703 0.6684 0.976 0.000 0.000 0.000 0.024
#> SRR1500661 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1080294 2 0.4803 -0.0565 0.000 0.496 0.012 0.488 0.004
#> SRR1336314 4 0.0162 0.8962 0.000 0.004 0.000 0.996 0.000
#> SRR1102152 1 0.2661 0.6825 0.888 0.000 0.056 0.000 0.056
#> SRR1345244 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1478637 1 0.6623 0.2603 0.452 0.000 0.300 0.000 0.248
#> SRR1443776 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1120939 3 0.2024 0.7748 0.068 0.004 0.920 0.004 0.004
#> SRR1080117 3 0.1544 0.7771 0.068 0.000 0.932 0.000 0.000
#> SRR1102899 2 0.0451 0.8945 0.000 0.988 0.004 0.008 0.000
#> SRR1091865 1 0.2079 0.6806 0.916 0.000 0.020 0.000 0.064
#> SRR1361072 1 0.0609 0.6693 0.980 0.000 0.000 0.000 0.020
#> SRR1487890 1 0.2230 0.6229 0.884 0.000 0.000 0.000 0.116
#> SRR1349456 3 0.3609 0.7577 0.080 0.032 0.848 0.000 0.040
#> SRR1389384 1 0.5665 0.5386 0.620 0.004 0.268 0.000 0.108
#> SRR1316096 2 0.0486 0.8929 0.000 0.988 0.004 0.004 0.004
#> SRR1408512 1 0.2361 0.6879 0.892 0.000 0.096 0.000 0.012
#> SRR1447547 3 0.6391 0.1431 0.028 0.000 0.460 0.428 0.084
#> SRR1354053 4 0.4305 0.5962 0.000 0.296 0.012 0.688 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.1444 0.7203 0.928 0.000 0.000 0.000 0.072 0.000
#> SRR1349562 1 0.2260 0.6919 0.860 0.000 0.000 0.000 0.140 0.000
#> SRR1353376 6 0.4293 0.7119 0.004 0.000 0.164 0.096 0.000 0.736
#> SRR1499040 1 0.6689 0.3010 0.484 0.000 0.212 0.000 0.240 0.064
#> SRR1322312 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1324412 3 0.5423 0.1809 0.452 0.000 0.460 0.000 0.072 0.016
#> SRR1100991 3 0.5423 0.1809 0.452 0.000 0.460 0.000 0.072 0.016
#> SRR1349479 6 0.4293 0.7119 0.004 0.000 0.164 0.096 0.000 0.736
#> SRR1431248 1 0.5749 0.4300 0.584 0.000 0.280 0.000 0.048 0.088
#> SRR1405054 1 0.5390 0.1068 0.544 0.000 0.356 0.000 0.088 0.012
#> SRR1312266 1 0.1082 0.7368 0.956 0.000 0.004 0.000 0.040 0.000
#> SRR1409790 3 0.5423 0.1809 0.452 0.000 0.460 0.000 0.072 0.016
#> SRR1352507 3 0.5423 0.1809 0.452 0.000 0.460 0.000 0.072 0.016
#> SRR1383763 5 0.2100 1.0000 0.112 0.000 0.004 0.000 0.884 0.000
#> SRR1468314 2 0.2263 0.8066 0.000 0.900 0.004 0.060 0.000 0.036
#> SRR1473674 2 0.0547 0.8591 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1390499 1 0.1075 0.7265 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR821043 4 0.1003 0.8645 0.000 0.020 0.000 0.964 0.000 0.016
#> SRR1455653 4 0.3916 0.5991 0.000 0.300 0.000 0.680 0.000 0.020
#> SRR1335236 2 0.0000 0.8617 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1095383 2 0.4337 -0.0238 0.000 0.500 0.000 0.480 0.000 0.020
#> SRR1479489 1 0.2407 0.7348 0.892 0.000 0.048 0.000 0.056 0.004
#> SRR1310433 2 0.0000 0.8617 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1073435 3 0.3143 0.4668 0.012 0.028 0.860 0.000 0.020 0.080
#> SRR659649 3 0.0520 0.5470 0.000 0.000 0.984 0.000 0.008 0.008
#> SRR1395999 1 0.0767 0.7387 0.976 0.000 0.008 0.000 0.012 0.004
#> SRR1105248 6 0.6328 0.7074 0.000 0.000 0.180 0.308 0.032 0.480
#> SRR1338257 1 0.1701 0.7327 0.920 0.000 0.000 0.000 0.072 0.008
#> SRR1499395 3 0.3533 0.5400 0.196 0.000 0.776 0.000 0.020 0.008
#> SRR1350002 2 0.2762 0.7658 0.000 0.804 0.000 0.000 0.000 0.196
#> SRR1489757 3 0.5423 0.1809 0.452 0.000 0.460 0.000 0.072 0.016
#> SRR1414637 1 0.6740 0.2151 0.460 0.000 0.312 0.000 0.084 0.144
#> SRR1478113 4 0.1219 0.8582 0.000 0.000 0.000 0.948 0.004 0.048
#> SRR1322477 1 0.5454 0.5399 0.652 0.000 0.208 0.000 0.064 0.076
#> SRR1478789 3 0.0000 0.5476 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1414185 3 0.0547 0.5400 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1069141 2 0.0000 0.8617 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 1 0.3818 0.6636 0.792 0.000 0.144 0.000 0.032 0.032
#> SRR1323491 1 0.0790 0.7335 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR1338103 1 0.5647 0.3182 0.544 0.000 0.348 0.000 0.044 0.064
#> SRR1472012 1 0.5921 0.4000 0.572 0.000 0.280 0.000 0.080 0.068
#> SRR1340325 1 0.2263 0.7370 0.900 0.000 0.036 0.000 0.060 0.004
#> SRR1087321 3 0.0000 0.5476 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1488790 1 0.1387 0.7216 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1334866 1 0.6542 0.2206 0.476 0.000 0.328 0.000 0.084 0.112
#> SRR1089446 3 0.5530 0.2506 0.420 0.000 0.480 0.000 0.084 0.016
#> SRR1344445 3 0.5333 0.2166 0.440 0.000 0.480 0.000 0.064 0.016
#> SRR1412969 3 0.0547 0.5400 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1071668 1 0.5647 -0.1725 0.456 0.000 0.432 0.000 0.096 0.016
#> SRR1075804 1 0.2527 0.7296 0.884 0.000 0.064 0.000 0.048 0.004
#> SRR1383283 3 0.3143 0.4668 0.012 0.028 0.860 0.000 0.020 0.080
#> SRR1350239 6 0.6449 0.7106 0.008 0.000 0.160 0.312 0.032 0.488
#> SRR1353878 1 0.1615 0.7344 0.928 0.000 0.004 0.000 0.064 0.004
#> SRR1375721 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1083983 1 0.6137 0.3263 0.536 0.000 0.304 0.000 0.088 0.072
#> SRR1090095 1 0.2053 0.7044 0.888 0.000 0.000 0.000 0.108 0.004
#> SRR1414792 1 0.2053 0.7044 0.888 0.000 0.000 0.000 0.108 0.004
#> SRR1075102 4 0.1219 0.8582 0.000 0.000 0.000 0.948 0.004 0.048
#> SRR1098737 1 0.2527 0.7296 0.884 0.000 0.064 0.000 0.048 0.004
#> SRR1349409 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1413008 6 0.6449 0.7106 0.008 0.000 0.160 0.312 0.032 0.488
#> SRR1407179 3 0.6019 0.1368 0.388 0.000 0.480 0.000 0.068 0.064
#> SRR1095913 3 0.3218 0.4635 0.012 0.032 0.856 0.000 0.020 0.080
#> SRR1403544 1 0.1814 0.7052 0.900 0.000 0.000 0.000 0.100 0.000
#> SRR1490546 1 0.0547 0.7332 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR807971 3 0.5333 0.2166 0.440 0.000 0.480 0.000 0.064 0.016
#> SRR1436228 3 0.5981 0.2955 0.324 0.000 0.536 0.000 0.068 0.072
#> SRR1445218 2 0.0000 0.8617 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1485438 2 0.3023 0.7462 0.004 0.784 0.000 0.000 0.000 0.212
#> SRR1358143 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1328760 1 0.1615 0.7344 0.928 0.000 0.004 0.000 0.064 0.004
#> SRR1380806 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1379426 3 0.0547 0.5400 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1087007 3 0.0547 0.5400 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1086256 1 0.6740 0.2151 0.460 0.000 0.312 0.000 0.084 0.144
#> SRR1346734 4 0.0000 0.8721 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.2219 0.6922 0.864 0.000 0.000 0.000 0.136 0.000
#> SRR1082151 1 0.6333 0.4093 0.552 0.000 0.244 0.000 0.084 0.120
#> SRR1349320 4 0.1219 0.8582 0.000 0.000 0.000 0.948 0.004 0.048
#> SRR1317554 4 0.1528 0.8551 0.000 0.048 0.000 0.936 0.000 0.016
#> SRR1076022 2 0.0146 0.8608 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1339573 3 0.3623 0.5394 0.208 0.000 0.764 0.000 0.020 0.008
#> SRR1455878 1 0.3603 0.6700 0.804 0.000 0.136 0.000 0.048 0.012
#> SRR1446203 3 0.0665 0.5460 0.000 0.004 0.980 0.000 0.008 0.008
#> SRR1387397 1 0.5291 0.5209 0.652 0.000 0.232 0.000 0.060 0.056
#> SRR1402590 1 0.1444 0.7203 0.928 0.000 0.000 0.000 0.072 0.000
#> SRR1317532 1 0.2278 0.7374 0.900 0.000 0.052 0.000 0.044 0.004
#> SRR1331488 1 0.4813 0.6502 0.756 0.000 0.036 0.032 0.112 0.064
#> SRR1499675 1 0.5499 0.2905 0.536 0.000 0.372 0.000 0.044 0.048
#> SRR1440467 6 0.3860 0.4111 0.000 0.000 0.472 0.000 0.000 0.528
#> SRR807995 2 0.2762 0.7658 0.000 0.804 0.000 0.000 0.000 0.196
#> SRR1476485 4 0.0000 0.8721 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.2261 0.7259 0.884 0.000 0.004 0.000 0.104 0.008
#> SRR1456051 1 0.1075 0.7265 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1473275 3 0.3834 0.5360 0.216 0.000 0.748 0.000 0.028 0.008
#> SRR1444083 1 0.1588 0.7329 0.924 0.000 0.000 0.000 0.072 0.004
#> SRR1313807 3 0.3143 0.4668 0.012 0.028 0.860 0.000 0.020 0.080
#> SRR1470751 1 0.6333 0.4093 0.552 0.000 0.244 0.000 0.084 0.120
#> SRR1403434 3 0.3634 -0.1566 0.000 0.000 0.644 0.000 0.000 0.356
#> SRR1390540 1 0.0790 0.7335 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR1093861 2 0.0547 0.8591 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1325290 1 0.6014 0.3798 0.560 0.000 0.284 0.000 0.092 0.064
#> SRR1070689 1 0.2300 0.6885 0.856 0.000 0.000 0.000 0.144 0.000
#> SRR1384049 5 0.2100 1.0000 0.112 0.000 0.004 0.000 0.884 0.000
#> SRR1081184 1 0.2260 0.6919 0.860 0.000 0.000 0.000 0.140 0.000
#> SRR1324295 1 0.1814 0.7052 0.900 0.000 0.000 0.000 0.100 0.000
#> SRR1365313 3 0.5556 0.3833 0.296 0.004 0.596 0.000 0.064 0.040
#> SRR1321877 3 0.0000 0.5476 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR815711 3 0.5613 0.2079 0.436 0.000 0.456 0.000 0.092 0.016
#> SRR1433476 6 0.4293 0.7119 0.004 0.000 0.164 0.096 0.000 0.736
#> SRR1101883 3 0.5333 0.2166 0.440 0.000 0.480 0.000 0.064 0.016
#> SRR1433729 3 0.3771 0.3360 0.000 0.048 0.792 0.000 0.016 0.144
#> SRR1341877 1 0.5647 0.3182 0.544 0.000 0.348 0.000 0.044 0.064
#> SRR1090556 1 0.5890 0.2509 0.512 0.000 0.364 0.000 0.060 0.064
#> SRR1357389 3 0.5345 0.1858 0.452 0.000 0.464 0.000 0.072 0.012
#> SRR1404227 3 0.3143 0.4715 0.012 0.028 0.860 0.000 0.020 0.080
#> SRR1376830 1 0.0632 0.7326 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1500661 1 0.2593 0.6905 0.844 0.000 0.008 0.000 0.148 0.000
#> SRR1080294 2 0.4337 -0.0238 0.000 0.500 0.000 0.480 0.000 0.020
#> SRR1336314 4 0.0000 0.8721 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1102152 1 0.2373 0.7242 0.880 0.000 0.008 0.000 0.104 0.008
#> SRR1345244 3 0.0000 0.5476 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1478637 1 0.7081 0.1200 0.412 0.000 0.280 0.000 0.220 0.088
#> SRR1443776 3 0.0000 0.5476 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1120939 3 0.0665 0.5460 0.000 0.004 0.980 0.000 0.008 0.008
#> SRR1080117 3 0.0547 0.5400 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1102899 2 0.0146 0.8608 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1091865 1 0.1895 0.7317 0.912 0.000 0.000 0.000 0.072 0.016
#> SRR1361072 1 0.0547 0.7332 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1487890 1 0.2340 0.6871 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1349456 3 0.3090 0.4697 0.012 0.028 0.864 0.000 0.020 0.076
#> SRR1389384 1 0.6333 0.4093 0.552 0.000 0.244 0.000 0.084 0.120
#> SRR1316096 2 0.0547 0.8591 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1408512 1 0.3075 0.6905 0.844 0.000 0.108 0.000 0.040 0.008
#> SRR1447547 6 0.6449 0.7106 0.008 0.000 0.160 0.312 0.032 0.488
#> SRR1354053 4 0.3853 0.5937 0.000 0.304 0.000 0.680 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.751 0.875 0.945 0.4694 0.515 0.515
#> 3 3 0.924 0.904 0.958 0.3674 0.693 0.480
#> 4 4 0.688 0.740 0.826 0.1152 0.872 0.660
#> 5 5 0.715 0.707 0.797 0.0812 0.929 0.750
#> 6 6 0.737 0.650 0.773 0.0475 0.935 0.732
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.9636 1.000 0.000
#> SRR1349562 1 0.0000 0.9636 1.000 0.000
#> SRR1353376 2 0.0000 0.9006 0.000 1.000
#> SRR1499040 1 0.0000 0.9636 1.000 0.000
#> SRR1322312 1 0.0000 0.9636 1.000 0.000
#> SRR1324412 1 0.0000 0.9636 1.000 0.000
#> SRR1100991 1 0.0000 0.9636 1.000 0.000
#> SRR1349479 2 0.0000 0.9006 0.000 1.000
#> SRR1431248 1 0.7815 0.6555 0.768 0.232
#> SRR1405054 1 0.0000 0.9636 1.000 0.000
#> SRR1312266 1 0.0000 0.9636 1.000 0.000
#> SRR1409790 1 0.0000 0.9636 1.000 0.000
#> SRR1352507 1 0.0000 0.9636 1.000 0.000
#> SRR1383763 1 0.0000 0.9636 1.000 0.000
#> SRR1468314 2 0.0000 0.9006 0.000 1.000
#> SRR1473674 2 0.0000 0.9006 0.000 1.000
#> SRR1390499 1 0.0000 0.9636 1.000 0.000
#> SRR821043 2 0.0000 0.9006 0.000 1.000
#> SRR1455653 2 0.0000 0.9006 0.000 1.000
#> SRR1335236 2 0.0000 0.9006 0.000 1.000
#> SRR1095383 2 0.0000 0.9006 0.000 1.000
#> SRR1479489 1 0.0000 0.9636 1.000 0.000
#> SRR1310433 2 0.0000 0.9006 0.000 1.000
#> SRR1073435 2 0.0376 0.8994 0.004 0.996
#> SRR659649 1 0.9998 -0.1594 0.508 0.492
#> SRR1395999 1 0.0000 0.9636 1.000 0.000
#> SRR1105248 2 0.0938 0.8967 0.012 0.988
#> SRR1338257 1 0.0000 0.9636 1.000 0.000
#> SRR1499395 1 0.0000 0.9636 1.000 0.000
#> SRR1350002 2 0.0000 0.9006 0.000 1.000
#> SRR1489757 1 0.0000 0.9636 1.000 0.000
#> SRR1414637 1 0.8555 0.5571 0.720 0.280
#> SRR1478113 2 0.0000 0.9006 0.000 1.000
#> SRR1322477 1 0.0000 0.9636 1.000 0.000
#> SRR1478789 2 0.7950 0.7500 0.240 0.760
#> SRR1414185 2 0.9170 0.6083 0.332 0.668
#> SRR1069141 2 0.0000 0.9006 0.000 1.000
#> SRR1376852 1 0.0000 0.9636 1.000 0.000
#> SRR1323491 1 0.0000 0.9636 1.000 0.000
#> SRR1338103 1 0.0000 0.9636 1.000 0.000
#> SRR1472012 1 0.0000 0.9636 1.000 0.000
#> SRR1340325 1 0.0000 0.9636 1.000 0.000
#> SRR1087321 2 0.4161 0.8657 0.084 0.916
#> SRR1488790 1 0.0000 0.9636 1.000 0.000
#> SRR1334866 1 0.9983 -0.0678 0.524 0.476
#> SRR1089446 1 0.0000 0.9636 1.000 0.000
#> SRR1344445 1 0.0000 0.9636 1.000 0.000
#> SRR1412969 2 0.6887 0.7989 0.184 0.816
#> SRR1071668 1 0.0000 0.9636 1.000 0.000
#> SRR1075804 1 0.0000 0.9636 1.000 0.000
#> SRR1383283 2 0.0000 0.9006 0.000 1.000
#> SRR1350239 2 0.9996 0.2036 0.488 0.512
#> SRR1353878 1 0.0000 0.9636 1.000 0.000
#> SRR1375721 1 0.0000 0.9636 1.000 0.000
#> SRR1083983 1 0.0000 0.9636 1.000 0.000
#> SRR1090095 1 0.0000 0.9636 1.000 0.000
#> SRR1414792 1 0.0000 0.9636 1.000 0.000
#> SRR1075102 2 0.0000 0.9006 0.000 1.000
#> SRR1098737 1 0.0000 0.9636 1.000 0.000
#> SRR1349409 1 0.0000 0.9636 1.000 0.000
#> SRR1413008 2 0.9996 0.2036 0.488 0.512
#> SRR1407179 1 0.0000 0.9636 1.000 0.000
#> SRR1095913 2 0.0000 0.9006 0.000 1.000
#> SRR1403544 1 0.0000 0.9636 1.000 0.000
#> SRR1490546 1 0.0000 0.9636 1.000 0.000
#> SRR807971 1 0.0000 0.9636 1.000 0.000
#> SRR1436228 1 0.7056 0.7248 0.808 0.192
#> SRR1445218 2 0.0000 0.9006 0.000 1.000
#> SRR1485438 2 0.4431 0.8603 0.092 0.908
#> SRR1358143 1 0.0000 0.9636 1.000 0.000
#> SRR1328760 1 0.0000 0.9636 1.000 0.000
#> SRR1380806 1 0.0000 0.9636 1.000 0.000
#> SRR1379426 2 0.8327 0.7202 0.264 0.736
#> SRR1087007 2 0.8081 0.7419 0.248 0.752
#> SRR1086256 2 0.0000 0.9006 0.000 1.000
#> SRR1346734 2 0.0000 0.9006 0.000 1.000
#> SRR1414515 1 0.0000 0.9636 1.000 0.000
#> SRR1082151 1 0.7376 0.6970 0.792 0.208
#> SRR1349320 2 0.0000 0.9006 0.000 1.000
#> SRR1317554 2 0.0000 0.9006 0.000 1.000
#> SRR1076022 2 0.0000 0.9006 0.000 1.000
#> SRR1339573 1 0.0000 0.9636 1.000 0.000
#> SRR1455878 1 0.0000 0.9636 1.000 0.000
#> SRR1446203 2 0.8081 0.7419 0.248 0.752
#> SRR1387397 1 0.0000 0.9636 1.000 0.000
#> SRR1402590 1 0.0000 0.9636 1.000 0.000
#> SRR1317532 1 0.0000 0.9636 1.000 0.000
#> SRR1331488 1 0.0000 0.9636 1.000 0.000
#> SRR1499675 1 0.0000 0.9636 1.000 0.000
#> SRR1440467 2 0.4022 0.8676 0.080 0.920
#> SRR807995 2 0.0000 0.9006 0.000 1.000
#> SRR1476485 2 0.0000 0.9006 0.000 1.000
#> SRR1388214 1 0.0000 0.9636 1.000 0.000
#> SRR1456051 1 0.0000 0.9636 1.000 0.000
#> SRR1473275 1 0.0000 0.9636 1.000 0.000
#> SRR1444083 1 0.0000 0.9636 1.000 0.000
#> SRR1313807 2 0.0000 0.9006 0.000 1.000
#> SRR1470751 1 0.6531 0.7605 0.832 0.168
#> SRR1403434 2 0.6343 0.8168 0.160 0.840
#> SRR1390540 1 0.0000 0.9636 1.000 0.000
#> SRR1093861 2 0.0000 0.9006 0.000 1.000
#> SRR1325290 1 0.0000 0.9636 1.000 0.000
#> SRR1070689 1 0.0000 0.9636 1.000 0.000
#> SRR1384049 1 0.0000 0.9636 1.000 0.000
#> SRR1081184 1 0.0000 0.9636 1.000 0.000
#> SRR1324295 1 0.0000 0.9636 1.000 0.000
#> SRR1365313 2 0.8081 0.7419 0.248 0.752
#> SRR1321877 2 0.8081 0.7419 0.248 0.752
#> SRR815711 1 0.0000 0.9636 1.000 0.000
#> SRR1433476 2 0.2423 0.8859 0.040 0.960
#> SRR1101883 1 0.0000 0.9636 1.000 0.000
#> SRR1433729 2 0.0000 0.9006 0.000 1.000
#> SRR1341877 1 0.0000 0.9636 1.000 0.000
#> SRR1090556 1 0.0000 0.9636 1.000 0.000
#> SRR1357389 1 0.0000 0.9636 1.000 0.000
#> SRR1404227 2 0.8386 0.7145 0.268 0.732
#> SRR1376830 1 0.0000 0.9636 1.000 0.000
#> SRR1500661 1 0.0000 0.9636 1.000 0.000
#> SRR1080294 2 0.0000 0.9006 0.000 1.000
#> SRR1336314 2 0.0000 0.9006 0.000 1.000
#> SRR1102152 1 0.0000 0.9636 1.000 0.000
#> SRR1345244 2 0.8081 0.7419 0.248 0.752
#> SRR1478637 1 0.9996 -0.1124 0.512 0.488
#> SRR1443776 2 0.8081 0.7419 0.248 0.752
#> SRR1120939 2 0.8081 0.7419 0.248 0.752
#> SRR1080117 2 0.8327 0.7202 0.264 0.736
#> SRR1102899 2 0.0000 0.9006 0.000 1.000
#> SRR1091865 1 0.0000 0.9636 1.000 0.000
#> SRR1361072 1 0.0000 0.9636 1.000 0.000
#> SRR1487890 1 0.0000 0.9636 1.000 0.000
#> SRR1349456 2 0.0000 0.9006 0.000 1.000
#> SRR1389384 1 0.0000 0.9636 1.000 0.000
#> SRR1316096 2 0.0000 0.9006 0.000 1.000
#> SRR1408512 1 0.0000 0.9636 1.000 0.000
#> SRR1447547 2 0.7815 0.7578 0.232 0.768
#> SRR1354053 2 0.0000 0.9006 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1499040 1 0.3816 0.8243 0.852 0.000 0.148
#> SRR1322312 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1324412 3 0.1529 0.9149 0.040 0.000 0.960
#> SRR1100991 3 0.1031 0.9266 0.024 0.000 0.976
#> SRR1349479 3 0.5465 0.6104 0.000 0.288 0.712
#> SRR1431248 3 0.6410 0.2585 0.420 0.004 0.576
#> SRR1405054 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1409790 3 0.1031 0.9266 0.024 0.000 0.976
#> SRR1352507 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1383763 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1468314 2 0.0424 0.9933 0.000 0.992 0.008
#> SRR1473674 2 0.0747 0.9907 0.000 0.984 0.016
#> SRR1390499 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR821043 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1455653 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1335236 2 0.0892 0.9907 0.000 0.980 0.020
#> SRR1095383 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1479489 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1310433 2 0.0747 0.9924 0.000 0.984 0.016
#> SRR1073435 3 0.0237 0.9293 0.000 0.004 0.996
#> SRR659649 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1395999 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1105248 3 0.0747 0.9265 0.000 0.016 0.984
#> SRR1338257 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1499395 3 0.0424 0.9314 0.008 0.000 0.992
#> SRR1350002 2 0.0747 0.9907 0.000 0.984 0.016
#> SRR1489757 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1414637 1 0.6398 0.4101 0.620 0.008 0.372
#> SRR1478113 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1322477 1 0.2400 0.9083 0.932 0.004 0.064
#> SRR1478789 3 0.0000 0.9292 0.000 0.000 1.000
#> SRR1414185 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1069141 2 0.0892 0.9907 0.000 0.980 0.020
#> SRR1376852 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1338103 1 0.6386 0.3040 0.584 0.004 0.412
#> SRR1472012 1 0.5678 0.5502 0.684 0.000 0.316
#> SRR1340325 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1087321 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1488790 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1334866 3 0.0475 0.9310 0.004 0.004 0.992
#> SRR1089446 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1344445 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1412969 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1071668 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1075804 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1383283 3 0.2448 0.8788 0.000 0.076 0.924
#> SRR1350239 3 0.0983 0.9287 0.004 0.016 0.980
#> SRR1353878 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1083983 1 0.1289 0.9362 0.968 0.000 0.032
#> SRR1090095 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1413008 3 0.0983 0.9287 0.004 0.016 0.980
#> SRR1407179 3 0.0424 0.9314 0.008 0.000 0.992
#> SRR1095913 3 0.2625 0.8609 0.000 0.084 0.916
#> SRR1403544 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR807971 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1436228 3 0.1267 0.9227 0.024 0.004 0.972
#> SRR1445218 2 0.0747 0.9924 0.000 0.984 0.016
#> SRR1485438 3 0.7067 0.1060 0.020 0.468 0.512
#> SRR1358143 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1379426 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1087007 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1086256 3 0.6260 0.2222 0.000 0.448 0.552
#> SRR1346734 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1082151 1 0.4465 0.7927 0.820 0.004 0.176
#> SRR1349320 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1317554 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1076022 2 0.0892 0.9907 0.000 0.980 0.020
#> SRR1339573 3 0.0424 0.9314 0.008 0.000 0.992
#> SRR1455878 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1446203 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1387397 1 0.2448 0.8996 0.924 0.000 0.076
#> SRR1402590 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1499675 3 0.1529 0.9145 0.040 0.000 0.960
#> SRR1440467 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR807995 2 0.0747 0.9907 0.000 0.984 0.016
#> SRR1476485 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1388214 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1473275 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1444083 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1313807 3 0.2625 0.8719 0.000 0.084 0.916
#> SRR1470751 1 0.2860 0.8911 0.912 0.004 0.084
#> SRR1403434 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1390540 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1093861 2 0.0892 0.9907 0.000 0.980 0.020
#> SRR1325290 1 0.4002 0.8099 0.840 0.000 0.160
#> SRR1070689 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1365313 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1321877 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR815711 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1433476 3 0.2711 0.8744 0.000 0.088 0.912
#> SRR1101883 3 0.0892 0.9286 0.020 0.000 0.980
#> SRR1433729 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1341877 3 0.6180 0.2784 0.416 0.000 0.584
#> SRR1090556 3 0.6295 0.0882 0.472 0.000 0.528
#> SRR1357389 3 0.0747 0.9298 0.016 0.000 0.984
#> SRR1404227 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1376830 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1080294 2 0.0237 0.9935 0.000 0.996 0.004
#> SRR1336314 2 0.0000 0.9926 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1345244 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1478637 3 0.0475 0.9310 0.004 0.004 0.992
#> SRR1443776 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1120939 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1080117 3 0.0237 0.9316 0.004 0.000 0.996
#> SRR1102899 2 0.0892 0.9907 0.000 0.980 0.020
#> SRR1091865 1 0.1129 0.9437 0.976 0.004 0.020
#> SRR1361072 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9586 1.000 0.000 0.000
#> SRR1349456 3 0.0424 0.9253 0.000 0.008 0.992
#> SRR1389384 1 0.4351 0.7995 0.828 0.004 0.168
#> SRR1316096 2 0.0747 0.9924 0.000 0.984 0.016
#> SRR1408512 1 0.0747 0.9478 0.984 0.000 0.016
#> SRR1447547 3 0.0983 0.9287 0.004 0.016 0.980
#> SRR1354053 2 0.0237 0.9935 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.4977 0.74520 0.000 0.460 0.000 0.540
#> SRR1499040 2 0.6898 0.59720 0.360 0.524 0.116 0.000
#> SRR1322312 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1100991 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1349479 3 0.6783 0.27407 0.000 0.388 0.512 0.100
#> SRR1431248 2 0.6771 0.64813 0.248 0.600 0.152 0.000
#> SRR1405054 1 0.3485 0.81407 0.856 0.116 0.028 0.000
#> SRR1312266 1 0.0817 0.93331 0.976 0.024 0.000 0.000
#> SRR1409790 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1352507 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1383763 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.3942 0.83330 0.000 0.236 0.000 0.764
#> SRR1473674 4 0.1004 0.80961 0.000 0.024 0.004 0.972
#> SRR1390499 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR821043 4 0.4679 0.81829 0.000 0.352 0.000 0.648
#> SRR1455653 4 0.4713 0.81554 0.000 0.360 0.000 0.640
#> SRR1335236 4 0.1042 0.80821 0.000 0.020 0.008 0.972
#> SRR1095383 4 0.4431 0.83044 0.000 0.304 0.000 0.696
#> SRR1479489 1 0.1302 0.91882 0.956 0.044 0.000 0.000
#> SRR1310433 4 0.0000 0.81743 0.000 0.000 0.000 1.000
#> SRR1073435 3 0.5495 0.50486 0.000 0.348 0.624 0.028
#> SRR659649 3 0.0188 0.83199 0.000 0.004 0.996 0.000
#> SRR1395999 1 0.4193 0.54898 0.732 0.268 0.000 0.000
#> SRR1105248 3 0.4973 0.59557 0.000 0.348 0.644 0.008
#> SRR1338257 1 0.2281 0.87596 0.904 0.096 0.000 0.000
#> SRR1499395 3 0.0336 0.83216 0.000 0.008 0.992 0.000
#> SRR1350002 4 0.1004 0.80961 0.000 0.024 0.004 0.972
#> SRR1489757 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1414637 2 0.6777 0.65158 0.296 0.600 0.092 0.012
#> SRR1478113 2 0.4992 -0.69158 0.000 0.524 0.000 0.476
#> SRR1322477 2 0.5543 0.51569 0.424 0.556 0.020 0.000
#> SRR1478789 3 0.2125 0.81038 0.000 0.076 0.920 0.004
#> SRR1414185 3 0.1118 0.83351 0.000 0.036 0.964 0.000
#> SRR1069141 4 0.0592 0.81344 0.000 0.016 0.000 0.984
#> SRR1376852 1 0.3907 0.62873 0.768 0.232 0.000 0.000
#> SRR1323491 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1338103 2 0.6783 0.64942 0.304 0.572 0.124 0.000
#> SRR1472012 2 0.6574 0.61140 0.364 0.548 0.088 0.000
#> SRR1340325 1 0.1867 0.89486 0.928 0.072 0.000 0.000
#> SRR1087321 3 0.0921 0.83218 0.000 0.028 0.972 0.000
#> SRR1488790 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.5155 -0.00794 0.000 0.528 0.468 0.004
#> SRR1089446 3 0.2868 0.80647 0.000 0.136 0.864 0.000
#> SRR1344445 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1412969 3 0.0921 0.83218 0.000 0.028 0.972 0.000
#> SRR1071668 3 0.2530 0.81496 0.000 0.112 0.888 0.000
#> SRR1075804 1 0.0707 0.93575 0.980 0.020 0.000 0.000
#> SRR1383283 3 0.5657 0.54216 0.000 0.312 0.644 0.044
#> SRR1350239 3 0.4331 0.71057 0.000 0.288 0.712 0.000
#> SRR1353878 1 0.2281 0.87596 0.904 0.096 0.000 0.000
#> SRR1375721 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1083983 2 0.5668 0.47763 0.444 0.532 0.024 0.000
#> SRR1090095 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1075102 2 0.4981 -0.67658 0.000 0.536 0.000 0.464
#> SRR1098737 1 0.1302 0.92191 0.956 0.044 0.000 0.000
#> SRR1349409 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1413008 3 0.4331 0.71057 0.000 0.288 0.712 0.000
#> SRR1407179 3 0.4877 0.39876 0.000 0.408 0.592 0.000
#> SRR1095913 3 0.5188 0.68869 0.000 0.096 0.756 0.148
#> SRR1403544 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR807971 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1436228 2 0.6516 0.26966 0.048 0.588 0.344 0.020
#> SRR1445218 4 0.0000 0.81743 0.000 0.000 0.000 1.000
#> SRR1485438 2 0.6688 0.25983 0.000 0.536 0.096 0.368
#> SRR1358143 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.2081 0.88828 0.916 0.084 0.000 0.000
#> SRR1380806 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.1022 0.83329 0.000 0.032 0.968 0.000
#> SRR1087007 3 0.0921 0.83218 0.000 0.028 0.972 0.000
#> SRR1086256 2 0.6074 0.20016 0.000 0.600 0.340 0.060
#> SRR1346734 4 0.4790 0.80737 0.000 0.380 0.000 0.620
#> SRR1414515 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.6431 0.63212 0.324 0.604 0.060 0.012
#> SRR1349320 4 0.4898 0.78530 0.000 0.416 0.000 0.584
#> SRR1317554 4 0.4522 0.82765 0.000 0.320 0.000 0.680
#> SRR1076022 4 0.0895 0.81032 0.000 0.020 0.004 0.976
#> SRR1339573 3 0.0336 0.83216 0.000 0.008 0.992 0.000
#> SRR1455878 1 0.2973 0.81084 0.856 0.144 0.000 0.000
#> SRR1446203 3 0.0188 0.83207 0.000 0.004 0.996 0.000
#> SRR1387397 2 0.5821 0.50158 0.432 0.536 0.032 0.000
#> SRR1402590 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.2589 0.85004 0.884 0.116 0.000 0.000
#> SRR1331488 1 0.0188 0.94529 0.996 0.004 0.000 0.000
#> SRR1499675 3 0.6506 -0.02041 0.072 0.460 0.468 0.000
#> SRR1440467 3 0.1022 0.83287 0.000 0.032 0.968 0.000
#> SRR807995 4 0.2048 0.77116 0.000 0.064 0.008 0.928
#> SRR1476485 4 0.4790 0.80737 0.000 0.380 0.000 0.620
#> SRR1388214 1 0.2345 0.87117 0.900 0.100 0.000 0.000
#> SRR1456051 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.2469 0.81543 0.000 0.108 0.892 0.000
#> SRR1444083 1 0.2408 0.86588 0.896 0.104 0.000 0.000
#> SRR1313807 3 0.5280 0.70141 0.000 0.120 0.752 0.128
#> SRR1470751 2 0.5969 0.59128 0.360 0.600 0.028 0.012
#> SRR1403434 3 0.1022 0.83287 0.000 0.032 0.968 0.000
#> SRR1390540 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1093861 4 0.0895 0.81032 0.000 0.020 0.004 0.976
#> SRR1325290 2 0.6357 0.58348 0.388 0.544 0.068 0.000
#> SRR1070689 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.5453 0.32378 0.000 0.388 0.592 0.020
#> SRR1321877 3 0.0817 0.83265 0.000 0.024 0.976 0.000
#> SRR815711 3 0.2530 0.81496 0.000 0.112 0.888 0.000
#> SRR1433476 3 0.5720 0.57399 0.000 0.296 0.652 0.052
#> SRR1101883 3 0.2589 0.81343 0.000 0.116 0.884 0.000
#> SRR1433729 4 0.4382 0.83134 0.000 0.296 0.000 0.704
#> SRR1341877 2 0.7001 0.62738 0.244 0.576 0.180 0.000
#> SRR1090556 2 0.6890 0.65405 0.268 0.580 0.152 0.000
#> SRR1357389 3 0.2216 0.82035 0.000 0.092 0.908 0.000
#> SRR1404227 3 0.3172 0.74072 0.000 0.160 0.840 0.000
#> SRR1376830 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.4382 0.83138 0.000 0.296 0.000 0.704
#> SRR1336314 4 0.4804 0.80629 0.000 0.384 0.000 0.616
#> SRR1102152 1 0.1637 0.90617 0.940 0.060 0.000 0.000
#> SRR1345244 3 0.0817 0.83265 0.000 0.024 0.976 0.000
#> SRR1478637 2 0.5366 0.06263 0.000 0.548 0.440 0.012
#> SRR1443776 3 0.0817 0.83265 0.000 0.024 0.976 0.000
#> SRR1120939 3 0.0336 0.83261 0.000 0.008 0.992 0.000
#> SRR1080117 3 0.1022 0.83329 0.000 0.032 0.968 0.000
#> SRR1102899 4 0.0188 0.81681 0.000 0.004 0.000 0.996
#> SRR1091865 2 0.5497 0.43725 0.460 0.524 0.016 0.000
#> SRR1361072 1 0.0188 0.94529 0.996 0.004 0.000 0.000
#> SRR1487890 1 0.0000 0.94708 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.3833 0.77432 0.000 0.080 0.848 0.072
#> SRR1389384 2 0.6642 0.62389 0.344 0.576 0.068 0.012
#> SRR1316096 4 0.0469 0.81479 0.000 0.012 0.000 0.988
#> SRR1408512 2 0.5497 0.43725 0.460 0.524 0.016 0.000
#> SRR1447547 2 0.4164 0.25290 0.000 0.736 0.264 0.000
#> SRR1354053 4 0.4522 0.82765 0.000 0.320 0.000 0.680
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0510 0.886 0.984 0.000 0.000 0.016 0.000
#> SRR1349562 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4780 0.586 0.000 0.248 0.000 0.692 0.060
#> SRR1499040 5 0.4947 0.709 0.068 0.000 0.160 0.028 0.744
#> SRR1322312 1 0.0290 0.884 0.992 0.000 0.000 0.008 0.000
#> SRR1324412 3 0.2233 0.737 0.000 0.000 0.892 0.004 0.104
#> SRR1100991 3 0.2358 0.734 0.000 0.000 0.888 0.008 0.104
#> SRR1349479 4 0.4494 0.279 0.000 0.012 0.232 0.728 0.028
#> SRR1431248 5 0.1934 0.823 0.040 0.000 0.008 0.020 0.932
#> SRR1405054 1 0.6903 0.562 0.564 0.000 0.244 0.072 0.120
#> SRR1312266 1 0.3639 0.836 0.824 0.000 0.000 0.076 0.100
#> SRR1409790 3 0.2233 0.737 0.000 0.000 0.892 0.004 0.104
#> SRR1352507 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1383763 1 0.0404 0.884 0.988 0.000 0.000 0.012 0.000
#> SRR1468314 2 0.4508 0.096 0.000 0.648 0.000 0.332 0.020
#> SRR1473674 2 0.0451 0.825 0.000 0.988 0.000 0.008 0.004
#> SRR1390499 1 0.0162 0.886 0.996 0.000 0.000 0.004 0.000
#> SRR821043 4 0.4299 0.543 0.000 0.388 0.000 0.608 0.004
#> SRR1455653 4 0.4299 0.543 0.000 0.388 0.000 0.608 0.004
#> SRR1335236 2 0.0324 0.827 0.000 0.992 0.000 0.004 0.004
#> SRR1095383 2 0.4648 -0.321 0.000 0.524 0.000 0.464 0.012
#> SRR1479489 1 0.3863 0.843 0.836 0.000 0.052 0.072 0.040
#> SRR1310433 2 0.0579 0.826 0.000 0.984 0.000 0.008 0.008
#> SRR1073435 5 0.6158 0.265 0.000 0.000 0.264 0.184 0.552
#> SRR659649 3 0.2951 0.784 0.000 0.000 0.860 0.112 0.028
#> SRR1395999 1 0.5933 0.402 0.524 0.000 0.012 0.076 0.388
#> SRR1105248 4 0.5338 -0.102 0.000 0.000 0.400 0.544 0.056
#> SRR1338257 1 0.5548 0.762 0.712 0.000 0.060 0.076 0.152
#> SRR1499395 3 0.3495 0.788 0.000 0.000 0.812 0.160 0.028
#> SRR1350002 2 0.0566 0.822 0.000 0.984 0.000 0.012 0.004
#> SRR1489757 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1414637 5 0.2790 0.822 0.052 0.008 0.008 0.036 0.896
#> SRR1478113 4 0.5082 0.564 0.000 0.220 0.000 0.684 0.096
#> SRR1322477 5 0.2734 0.806 0.076 0.000 0.008 0.028 0.888
#> SRR1478789 3 0.5382 0.717 0.000 0.000 0.656 0.224 0.120
#> SRR1414185 3 0.4133 0.789 0.000 0.000 0.768 0.180 0.052
#> SRR1069141 2 0.0324 0.830 0.000 0.992 0.000 0.004 0.004
#> SRR1376852 1 0.5425 0.343 0.520 0.000 0.000 0.060 0.420
#> SRR1323491 1 0.1270 0.882 0.948 0.000 0.000 0.052 0.000
#> SRR1338103 5 0.2576 0.824 0.056 0.000 0.008 0.036 0.900
#> SRR1472012 5 0.2433 0.825 0.056 0.000 0.012 0.024 0.908
#> SRR1340325 1 0.4712 0.816 0.784 0.000 0.064 0.076 0.076
#> SRR1087321 3 0.4269 0.785 0.000 0.000 0.756 0.188 0.056
#> SRR1488790 1 0.0290 0.886 0.992 0.000 0.000 0.008 0.000
#> SRR1334866 5 0.2983 0.777 0.000 0.000 0.076 0.056 0.868
#> SRR1089446 3 0.3521 0.608 0.000 0.000 0.764 0.004 0.232
#> SRR1344445 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1412969 3 0.4238 0.785 0.000 0.000 0.756 0.192 0.052
#> SRR1071668 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1075804 1 0.3586 0.839 0.828 0.000 0.000 0.076 0.096
#> SRR1383283 5 0.6464 0.176 0.000 0.004 0.284 0.196 0.516
#> SRR1350239 3 0.5073 0.534 0.000 0.000 0.688 0.212 0.100
#> SRR1353878 1 0.5485 0.765 0.716 0.000 0.056 0.076 0.152
#> SRR1375721 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 5 0.3270 0.791 0.080 0.000 0.020 0.036 0.864
#> SRR1090095 1 0.0162 0.886 0.996 0.000 0.000 0.004 0.000
#> SRR1414792 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.5004 0.564 0.000 0.216 0.000 0.692 0.092
#> SRR1098737 1 0.3791 0.830 0.812 0.000 0.000 0.076 0.112
#> SRR1349409 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 3 0.5073 0.534 0.000 0.000 0.688 0.212 0.100
#> SRR1407179 5 0.5288 0.488 0.000 0.000 0.244 0.100 0.656
#> SRR1095913 3 0.6867 0.650 0.000 0.064 0.568 0.236 0.132
#> SRR1403544 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.2172 0.873 0.908 0.000 0.000 0.076 0.016
#> SRR807971 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1436228 5 0.2200 0.809 0.008 0.004 0.032 0.032 0.924
#> SRR1445218 2 0.0579 0.826 0.000 0.984 0.000 0.008 0.008
#> SRR1485438 5 0.5206 0.318 0.000 0.416 0.004 0.036 0.544
#> SRR1358143 1 0.0404 0.884 0.988 0.000 0.000 0.012 0.000
#> SRR1328760 1 0.5275 0.778 0.732 0.000 0.048 0.076 0.144
#> SRR1380806 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.4133 0.789 0.000 0.000 0.768 0.180 0.052
#> SRR1087007 3 0.4168 0.788 0.000 0.000 0.764 0.184 0.052
#> SRR1086256 5 0.3046 0.777 0.000 0.020 0.028 0.076 0.876
#> SRR1346734 4 0.3999 0.587 0.000 0.344 0.000 0.656 0.000
#> SRR1414515 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.2769 0.817 0.068 0.008 0.004 0.028 0.892
#> SRR1349320 4 0.4520 0.593 0.000 0.284 0.000 0.684 0.032
#> SRR1317554 4 0.4533 0.434 0.000 0.448 0.000 0.544 0.008
#> SRR1076022 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000
#> SRR1339573 3 0.2951 0.784 0.000 0.000 0.860 0.112 0.028
#> SRR1455878 1 0.6469 0.579 0.584 0.000 0.064 0.076 0.276
#> SRR1446203 3 0.4065 0.788 0.000 0.000 0.772 0.180 0.048
#> SRR1387397 5 0.4146 0.765 0.068 0.000 0.048 0.064 0.820
#> SRR1402590 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.5863 0.722 0.676 0.000 0.060 0.076 0.188
#> SRR1331488 1 0.2006 0.876 0.916 0.000 0.000 0.072 0.012
#> SRR1499675 5 0.4085 0.728 0.008 0.000 0.104 0.084 0.804
#> SRR1440467 3 0.4234 0.787 0.000 0.000 0.760 0.184 0.056
#> SRR807995 2 0.0912 0.806 0.000 0.972 0.000 0.012 0.016
#> SRR1476485 4 0.3999 0.587 0.000 0.344 0.000 0.656 0.000
#> SRR1388214 1 0.5730 0.742 0.692 0.000 0.060 0.076 0.172
#> SRR1456051 1 0.1544 0.879 0.932 0.000 0.000 0.068 0.000
#> SRR1473275 3 0.2020 0.747 0.000 0.000 0.900 0.000 0.100
#> SRR1444083 1 0.5623 0.753 0.704 0.000 0.060 0.076 0.160
#> SRR1313807 3 0.6837 0.528 0.000 0.044 0.488 0.356 0.112
#> SRR1470751 5 0.2673 0.814 0.072 0.008 0.000 0.028 0.892
#> SRR1403434 3 0.4234 0.787 0.000 0.000 0.760 0.184 0.056
#> SRR1390540 1 0.1270 0.882 0.948 0.000 0.000 0.052 0.000
#> SRR1093861 2 0.0162 0.829 0.000 0.996 0.000 0.000 0.004
#> SRR1325290 5 0.2444 0.821 0.068 0.000 0.012 0.016 0.904
#> SRR1070689 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0404 0.884 0.988 0.000 0.000 0.012 0.000
#> SRR1081184 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 5 0.5841 0.416 0.000 0.000 0.256 0.148 0.596
#> SRR1321877 3 0.4269 0.785 0.000 0.000 0.756 0.188 0.056
#> SRR815711 3 0.2179 0.738 0.000 0.000 0.888 0.000 0.112
#> SRR1433476 4 0.5727 -0.235 0.000 0.008 0.384 0.540 0.068
#> SRR1101883 3 0.2074 0.739 0.000 0.000 0.896 0.000 0.104
#> SRR1433729 4 0.5124 0.335 0.000 0.480 0.004 0.488 0.028
#> SRR1341877 5 0.2780 0.816 0.032 0.000 0.032 0.040 0.896
#> SRR1090556 5 0.2342 0.818 0.040 0.000 0.024 0.020 0.916
#> SRR1357389 3 0.1544 0.747 0.000 0.000 0.932 0.000 0.068
#> SRR1404227 3 0.6464 0.373 0.000 0.000 0.476 0.200 0.324
#> SRR1376830 1 0.1341 0.881 0.944 0.000 0.000 0.056 0.000
#> SRR1500661 1 0.0162 0.886 0.996 0.000 0.000 0.004 0.000
#> SRR1080294 2 0.4735 -0.319 0.000 0.524 0.000 0.460 0.016
#> SRR1336314 4 0.3983 0.587 0.000 0.340 0.000 0.660 0.000
#> SRR1102152 1 0.4398 0.829 0.804 0.000 0.052 0.076 0.068
#> SRR1345244 3 0.4199 0.788 0.000 0.000 0.764 0.180 0.056
#> SRR1478637 5 0.2664 0.793 0.000 0.004 0.064 0.040 0.892
#> SRR1443776 3 0.4203 0.787 0.000 0.000 0.760 0.188 0.052
#> SRR1120939 3 0.4101 0.788 0.000 0.000 0.768 0.184 0.048
#> SRR1080117 3 0.4065 0.789 0.000 0.000 0.772 0.180 0.048
#> SRR1102899 2 0.0579 0.826 0.000 0.984 0.000 0.008 0.008
#> SRR1091865 5 0.3700 0.781 0.080 0.000 0.020 0.060 0.840
#> SRR1361072 1 0.3114 0.863 0.872 0.000 0.036 0.076 0.016
#> SRR1487890 1 0.0000 0.886 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.6433 0.683 0.000 0.044 0.604 0.232 0.120
#> SRR1389384 5 0.2527 0.817 0.072 0.004 0.004 0.020 0.900
#> SRR1316096 2 0.0162 0.829 0.000 0.996 0.000 0.004 0.000
#> SRR1408512 5 0.3706 0.779 0.076 0.000 0.020 0.064 0.840
#> SRR1447547 5 0.3319 0.730 0.000 0.000 0.020 0.160 0.820
#> SRR1354053 4 0.4552 0.384 0.000 0.468 0.000 0.524 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.2384 0.8183 0.884 0.000 0.000 0.032 0.000 0.084
#> SRR1349562 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1353376 4 0.4211 0.6862 0.000 0.088 0.004 0.788 0.040 0.080
#> SRR1499040 5 0.4103 0.7519 0.044 0.000 0.116 0.016 0.796 0.028
#> SRR1322312 1 0.4343 0.7853 0.724 0.000 0.004 0.084 0.000 0.188
#> SRR1324412 3 0.1148 0.7297 0.020 0.000 0.960 0.000 0.016 0.004
#> SRR1100991 3 0.1148 0.7297 0.020 0.000 0.960 0.000 0.016 0.004
#> SRR1349479 4 0.5548 0.1733 0.000 0.000 0.108 0.496 0.008 0.388
#> SRR1431248 5 0.1757 0.8390 0.008 0.000 0.000 0.012 0.928 0.052
#> SRR1405054 3 0.4968 0.0573 0.416 0.000 0.532 0.004 0.040 0.008
#> SRR1312266 1 0.2863 0.7592 0.864 0.000 0.000 0.036 0.088 0.012
#> SRR1409790 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1352507 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1383763 1 0.4498 0.7822 0.716 0.000 0.008 0.088 0.000 0.188
#> SRR1468314 2 0.4819 0.3254 0.000 0.648 0.004 0.276 0.004 0.068
#> SRR1473674 2 0.1268 0.7837 0.000 0.952 0.000 0.008 0.004 0.036
#> SRR1390499 1 0.3506 0.8154 0.792 0.000 0.000 0.052 0.000 0.156
#> SRR821043 4 0.3245 0.6606 0.000 0.228 0.000 0.764 0.000 0.008
#> SRR1455653 4 0.3190 0.6691 0.000 0.220 0.000 0.772 0.000 0.008
#> SRR1335236 2 0.0260 0.8026 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1095383 2 0.4873 -0.0714 0.000 0.508 0.000 0.440 0.004 0.048
#> SRR1479489 1 0.2846 0.7655 0.872 0.000 0.080 0.016 0.028 0.004
#> SRR1310433 2 0.0363 0.8043 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1073435 6 0.5658 0.0425 0.000 0.004 0.080 0.020 0.388 0.508
#> SRR659649 3 0.3647 -0.2765 0.000 0.000 0.640 0.000 0.000 0.360
#> SRR1395999 1 0.4955 0.2485 0.572 0.000 0.004 0.020 0.376 0.028
#> SRR1105248 4 0.6176 0.1133 0.000 0.000 0.204 0.436 0.012 0.348
#> SRR1338257 1 0.4541 0.6958 0.764 0.000 0.092 0.036 0.100 0.008
#> SRR1499395 3 0.4072 -0.5746 0.000 0.000 0.544 0.008 0.000 0.448
#> SRR1350002 2 0.1410 0.7786 0.000 0.944 0.000 0.008 0.004 0.044
#> SRR1489757 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1414637 5 0.1577 0.8332 0.008 0.000 0.000 0.016 0.940 0.036
#> SRR1478113 4 0.4057 0.6730 0.000 0.072 0.004 0.800 0.084 0.040
#> SRR1322477 5 0.2017 0.8238 0.048 0.000 0.004 0.020 0.920 0.008
#> SRR1478789 6 0.4620 0.6615 0.000 0.000 0.368 0.000 0.048 0.584
#> SRR1414185 6 0.4328 0.6937 0.000 0.000 0.460 0.020 0.000 0.520
#> SRR1069141 2 0.0000 0.8063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 5 0.5082 0.2241 0.408 0.000 0.004 0.020 0.536 0.032
#> SRR1323491 1 0.0806 0.8053 0.972 0.000 0.000 0.008 0.000 0.020
#> SRR1338103 5 0.2153 0.8340 0.008 0.000 0.004 0.004 0.900 0.084
#> SRR1472012 5 0.1621 0.8386 0.008 0.000 0.004 0.004 0.936 0.048
#> SRR1340325 1 0.3798 0.7351 0.816 0.000 0.096 0.024 0.056 0.008
#> SRR1087321 6 0.4331 0.6943 0.000 0.000 0.464 0.020 0.000 0.516
#> SRR1488790 1 0.2724 0.8191 0.864 0.000 0.000 0.052 0.000 0.084
#> SRR1334866 5 0.1970 0.8163 0.000 0.000 0.000 0.008 0.900 0.092
#> SRR1089446 3 0.2527 0.6470 0.000 0.000 0.876 0.000 0.084 0.040
#> SRR1344445 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1412969 6 0.4442 0.6911 0.000 0.000 0.440 0.020 0.004 0.536
#> SRR1071668 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1075804 1 0.3340 0.7459 0.840 0.000 0.004 0.024 0.100 0.032
#> SRR1383283 6 0.5829 0.2123 0.000 0.004 0.100 0.024 0.340 0.532
#> SRR1350239 3 0.5438 0.4393 0.012 0.000 0.648 0.212 0.016 0.112
#> SRR1353878 1 0.4302 0.7074 0.780 0.000 0.084 0.028 0.100 0.008
#> SRR1375721 1 0.3660 0.8127 0.780 0.000 0.000 0.060 0.000 0.160
#> SRR1083983 5 0.1542 0.8252 0.052 0.000 0.004 0.008 0.936 0.000
#> SRR1090095 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1414792 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1075102 4 0.4012 0.6695 0.000 0.064 0.004 0.804 0.084 0.044
#> SRR1098737 1 0.3370 0.7468 0.840 0.000 0.004 0.028 0.096 0.032
#> SRR1349409 1 0.3660 0.8127 0.780 0.000 0.000 0.060 0.000 0.160
#> SRR1413008 3 0.5438 0.4393 0.012 0.000 0.648 0.212 0.016 0.112
#> SRR1407179 5 0.5084 0.5627 0.000 0.000 0.100 0.012 0.644 0.244
#> SRR1095913 6 0.5823 0.5822 0.000 0.068 0.220 0.020 0.056 0.636
#> SRR1403544 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1490546 1 0.1434 0.7940 0.948 0.000 0.000 0.024 0.020 0.008
#> SRR807971 3 0.0458 0.7423 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1436228 5 0.2194 0.8295 0.004 0.000 0.004 0.004 0.892 0.096
#> SRR1445218 2 0.0363 0.8043 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1485438 5 0.5300 0.3751 0.000 0.356 0.000 0.016 0.556 0.072
#> SRR1358143 1 0.4343 0.7853 0.724 0.000 0.004 0.084 0.000 0.188
#> SRR1328760 1 0.3980 0.7243 0.804 0.000 0.060 0.028 0.100 0.008
#> SRR1380806 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1379426 6 0.4333 0.6908 0.000 0.000 0.468 0.020 0.000 0.512
#> SRR1087007 6 0.4331 0.6943 0.000 0.000 0.464 0.020 0.000 0.516
#> SRR1086256 5 0.2586 0.8047 0.000 0.004 0.004 0.020 0.876 0.096
#> SRR1346734 4 0.2703 0.7069 0.000 0.172 0.000 0.824 0.004 0.000
#> SRR1414515 1 0.3660 0.8127 0.780 0.000 0.000 0.060 0.000 0.160
#> SRR1082151 5 0.2213 0.8237 0.012 0.000 0.000 0.032 0.908 0.048
#> SRR1349320 4 0.3908 0.7025 0.000 0.140 0.004 0.792 0.024 0.040
#> SRR1317554 4 0.4482 0.2893 0.000 0.416 0.000 0.552 0.000 0.032
#> SRR1076022 2 0.0000 0.8063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339573 3 0.3647 -0.2802 0.000 0.000 0.640 0.000 0.000 0.360
#> SRR1455878 1 0.5774 0.4383 0.608 0.000 0.080 0.028 0.264 0.020
#> SRR1446203 6 0.4169 0.6779 0.000 0.000 0.456 0.012 0.000 0.532
#> SRR1387397 5 0.4488 0.7327 0.144 0.000 0.036 0.024 0.764 0.032
#> SRR1402590 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1317532 1 0.4995 0.6617 0.732 0.000 0.088 0.028 0.128 0.024
#> SRR1331488 1 0.2182 0.7894 0.916 0.000 0.004 0.032 0.028 0.020
#> SRR1499675 5 0.3403 0.7640 0.004 0.000 0.020 0.004 0.796 0.176
#> SRR1440467 6 0.4533 0.6888 0.000 0.000 0.432 0.020 0.008 0.540
#> SRR807995 2 0.1410 0.7786 0.000 0.944 0.000 0.008 0.004 0.044
#> SRR1476485 4 0.2738 0.7057 0.000 0.176 0.000 0.820 0.004 0.000
#> SRR1388214 1 0.4541 0.6958 0.764 0.000 0.092 0.036 0.100 0.008
#> SRR1456051 1 0.1124 0.8108 0.956 0.000 0.000 0.008 0.000 0.036
#> SRR1473275 3 0.0717 0.7357 0.000 0.000 0.976 0.000 0.016 0.008
#> SRR1444083 1 0.4586 0.6921 0.760 0.000 0.092 0.036 0.104 0.008
#> SRR1313807 6 0.5796 0.5038 0.000 0.072 0.120 0.064 0.056 0.688
#> SRR1470751 5 0.2213 0.8237 0.012 0.000 0.000 0.032 0.908 0.048
#> SRR1403434 6 0.4538 0.6898 0.000 0.000 0.436 0.020 0.008 0.536
#> SRR1390540 1 0.1065 0.8040 0.964 0.000 0.000 0.008 0.008 0.020
#> SRR1093861 2 0.0000 0.8063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1325290 5 0.1007 0.8387 0.008 0.000 0.004 0.004 0.968 0.016
#> SRR1070689 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1384049 1 0.4498 0.7822 0.716 0.000 0.008 0.088 0.000 0.188
#> SRR1081184 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1324295 1 0.3566 0.8147 0.788 0.000 0.000 0.056 0.000 0.156
#> SRR1365313 5 0.5019 0.3022 0.000 0.000 0.048 0.012 0.536 0.404
#> SRR1321877 6 0.4175 0.6940 0.000 0.000 0.464 0.012 0.000 0.524
#> SRR815711 3 0.1794 0.7018 0.000 0.000 0.924 0.000 0.036 0.040
#> SRR1433476 6 0.5917 0.2094 0.000 0.000 0.160 0.332 0.012 0.496
#> SRR1101883 3 0.0603 0.7410 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR1433729 2 0.6194 -0.1361 0.000 0.436 0.004 0.356 0.008 0.196
#> SRR1341877 5 0.2404 0.8291 0.008 0.000 0.004 0.004 0.880 0.104
#> SRR1090556 5 0.2276 0.8349 0.016 0.000 0.004 0.020 0.908 0.052
#> SRR1357389 3 0.0405 0.7335 0.000 0.000 0.988 0.000 0.008 0.004
#> SRR1404227 6 0.5768 0.5132 0.000 0.000 0.220 0.012 0.204 0.564
#> SRR1376830 1 0.2019 0.8180 0.900 0.000 0.000 0.012 0.000 0.088
#> SRR1500661 1 0.3812 0.8163 0.788 0.000 0.004 0.064 0.004 0.140
#> SRR1080294 2 0.5013 -0.0542 0.000 0.508 0.000 0.428 0.004 0.060
#> SRR1336314 4 0.2703 0.7069 0.000 0.172 0.000 0.824 0.004 0.000
#> SRR1102152 1 0.3666 0.7435 0.828 0.000 0.084 0.028 0.052 0.008
#> SRR1345244 6 0.4333 0.6908 0.000 0.000 0.468 0.020 0.000 0.512
#> SRR1478637 5 0.1668 0.8318 0.000 0.000 0.004 0.008 0.928 0.060
#> SRR1443776 6 0.4256 0.6945 0.000 0.000 0.464 0.016 0.000 0.520
#> SRR1120939 6 0.4165 0.6778 0.000 0.000 0.452 0.012 0.000 0.536
#> SRR1080117 6 0.4333 0.6908 0.000 0.000 0.468 0.020 0.000 0.512
#> SRR1102899 2 0.0547 0.8017 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1091865 5 0.2956 0.7912 0.092 0.000 0.004 0.036 0.860 0.008
#> SRR1361072 1 0.2298 0.7847 0.912 0.000 0.032 0.024 0.024 0.008
#> SRR1487890 1 0.3624 0.8134 0.784 0.000 0.000 0.060 0.000 0.156
#> SRR1349456 6 0.5737 0.5986 0.000 0.064 0.248 0.016 0.048 0.624
#> SRR1389384 5 0.1801 0.8304 0.012 0.000 0.004 0.012 0.932 0.040
#> SRR1316096 2 0.0000 0.8063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1408512 5 0.3617 0.7524 0.148 0.000 0.004 0.020 0.804 0.024
#> SRR1447547 5 0.5158 0.6017 0.008 0.000 0.008 0.188 0.668 0.128
#> SRR1354053 4 0.4253 0.1703 0.000 0.460 0.000 0.524 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.970 0.987 0.5021 0.499 0.499
#> 3 3 0.999 0.977 0.989 0.3130 0.781 0.587
#> 4 4 0.817 0.860 0.867 0.0932 0.919 0.769
#> 5 5 0.809 0.697 0.825 0.0621 0.865 0.591
#> 6 6 0.800 0.696 0.823 0.0479 0.924 0.706
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.983 1.000 0.000
#> SRR1349562 1 0.0000 0.983 1.000 0.000
#> SRR1353376 2 0.0000 0.991 0.000 1.000
#> SRR1499040 1 0.0000 0.983 1.000 0.000
#> SRR1322312 1 0.0000 0.983 1.000 0.000
#> SRR1324412 1 0.0000 0.983 1.000 0.000
#> SRR1100991 1 0.0000 0.983 1.000 0.000
#> SRR1349479 2 0.0000 0.991 0.000 1.000
#> SRR1431248 2 0.3114 0.936 0.056 0.944
#> SRR1405054 1 0.0000 0.983 1.000 0.000
#> SRR1312266 1 0.0000 0.983 1.000 0.000
#> SRR1409790 1 0.0000 0.983 1.000 0.000
#> SRR1352507 1 0.0000 0.983 1.000 0.000
#> SRR1383763 1 0.0000 0.983 1.000 0.000
#> SRR1468314 2 0.0000 0.991 0.000 1.000
#> SRR1473674 2 0.0000 0.991 0.000 1.000
#> SRR1390499 1 0.0000 0.983 1.000 0.000
#> SRR821043 2 0.0000 0.991 0.000 1.000
#> SRR1455653 2 0.0000 0.991 0.000 1.000
#> SRR1335236 2 0.0000 0.991 0.000 1.000
#> SRR1095383 2 0.0000 0.991 0.000 1.000
#> SRR1479489 1 0.0000 0.983 1.000 0.000
#> SRR1310433 2 0.0000 0.991 0.000 1.000
#> SRR1073435 2 0.0000 0.991 0.000 1.000
#> SRR659649 2 0.0376 0.987 0.004 0.996
#> SRR1395999 1 0.0000 0.983 1.000 0.000
#> SRR1105248 2 0.0000 0.991 0.000 1.000
#> SRR1338257 1 0.0000 0.983 1.000 0.000
#> SRR1499395 1 0.7299 0.751 0.796 0.204
#> SRR1350002 2 0.0000 0.991 0.000 1.000
#> SRR1489757 1 0.0000 0.983 1.000 0.000
#> SRR1414637 2 0.7056 0.764 0.192 0.808
#> SRR1478113 2 0.0000 0.991 0.000 1.000
#> SRR1322477 1 0.0000 0.983 1.000 0.000
#> SRR1478789 2 0.0000 0.991 0.000 1.000
#> SRR1414185 2 0.0000 0.991 0.000 1.000
#> SRR1069141 2 0.0000 0.991 0.000 1.000
#> SRR1376852 1 0.0000 0.983 1.000 0.000
#> SRR1323491 1 0.0000 0.983 1.000 0.000
#> SRR1338103 1 0.1414 0.965 0.980 0.020
#> SRR1472012 1 0.0000 0.983 1.000 0.000
#> SRR1340325 1 0.0000 0.983 1.000 0.000
#> SRR1087321 2 0.0000 0.991 0.000 1.000
#> SRR1488790 1 0.0000 0.983 1.000 0.000
#> SRR1334866 2 0.0000 0.991 0.000 1.000
#> SRR1089446 1 0.3114 0.931 0.944 0.056
#> SRR1344445 1 0.0000 0.983 1.000 0.000
#> SRR1412969 2 0.0000 0.991 0.000 1.000
#> SRR1071668 1 0.0000 0.983 1.000 0.000
#> SRR1075804 1 0.0000 0.983 1.000 0.000
#> SRR1383283 2 0.0000 0.991 0.000 1.000
#> SRR1350239 2 0.0000 0.991 0.000 1.000
#> SRR1353878 1 0.0000 0.983 1.000 0.000
#> SRR1375721 1 0.0000 0.983 1.000 0.000
#> SRR1083983 1 0.0000 0.983 1.000 0.000
#> SRR1090095 1 0.0000 0.983 1.000 0.000
#> SRR1414792 1 0.0000 0.983 1.000 0.000
#> SRR1075102 2 0.0000 0.991 0.000 1.000
#> SRR1098737 1 0.0000 0.983 1.000 0.000
#> SRR1349409 1 0.0000 0.983 1.000 0.000
#> SRR1413008 2 0.0000 0.991 0.000 1.000
#> SRR1407179 1 0.4562 0.888 0.904 0.096
#> SRR1095913 2 0.0000 0.991 0.000 1.000
#> SRR1403544 1 0.0000 0.983 1.000 0.000
#> SRR1490546 1 0.0000 0.983 1.000 0.000
#> SRR807971 1 0.0000 0.983 1.000 0.000
#> SRR1436228 2 0.0000 0.991 0.000 1.000
#> SRR1445218 2 0.0000 0.991 0.000 1.000
#> SRR1485438 2 0.0000 0.991 0.000 1.000
#> SRR1358143 1 0.0000 0.983 1.000 0.000
#> SRR1328760 1 0.0000 0.983 1.000 0.000
#> SRR1380806 1 0.0000 0.983 1.000 0.000
#> SRR1379426 2 0.0000 0.991 0.000 1.000
#> SRR1087007 2 0.0000 0.991 0.000 1.000
#> SRR1086256 2 0.0000 0.991 0.000 1.000
#> SRR1346734 2 0.0000 0.991 0.000 1.000
#> SRR1414515 1 0.0000 0.983 1.000 0.000
#> SRR1082151 2 0.4690 0.886 0.100 0.900
#> SRR1349320 2 0.0000 0.991 0.000 1.000
#> SRR1317554 2 0.0000 0.991 0.000 1.000
#> SRR1076022 2 0.0000 0.991 0.000 1.000
#> SRR1339573 1 0.9209 0.512 0.664 0.336
#> SRR1455878 1 0.0000 0.983 1.000 0.000
#> SRR1446203 2 0.0000 0.991 0.000 1.000
#> SRR1387397 1 0.0000 0.983 1.000 0.000
#> SRR1402590 1 0.0000 0.983 1.000 0.000
#> SRR1317532 1 0.0000 0.983 1.000 0.000
#> SRR1331488 1 0.0000 0.983 1.000 0.000
#> SRR1499675 1 0.9608 0.388 0.616 0.384
#> SRR1440467 2 0.0000 0.991 0.000 1.000
#> SRR807995 2 0.0000 0.991 0.000 1.000
#> SRR1476485 2 0.0000 0.991 0.000 1.000
#> SRR1388214 1 0.0000 0.983 1.000 0.000
#> SRR1456051 1 0.0000 0.983 1.000 0.000
#> SRR1473275 1 0.0000 0.983 1.000 0.000
#> SRR1444083 1 0.0000 0.983 1.000 0.000
#> SRR1313807 2 0.0000 0.991 0.000 1.000
#> SRR1470751 2 0.7139 0.761 0.196 0.804
#> SRR1403434 2 0.0000 0.991 0.000 1.000
#> SRR1390540 1 0.0000 0.983 1.000 0.000
#> SRR1093861 2 0.0000 0.991 0.000 1.000
#> SRR1325290 1 0.0000 0.983 1.000 0.000
#> SRR1070689 1 0.0000 0.983 1.000 0.000
#> SRR1384049 1 0.0000 0.983 1.000 0.000
#> SRR1081184 1 0.0000 0.983 1.000 0.000
#> SRR1324295 1 0.0000 0.983 1.000 0.000
#> SRR1365313 2 0.0000 0.991 0.000 1.000
#> SRR1321877 2 0.0000 0.991 0.000 1.000
#> SRR815711 1 0.0000 0.983 1.000 0.000
#> SRR1433476 2 0.0000 0.991 0.000 1.000
#> SRR1101883 1 0.0000 0.983 1.000 0.000
#> SRR1433729 2 0.0000 0.991 0.000 1.000
#> SRR1341877 1 0.0000 0.983 1.000 0.000
#> SRR1090556 1 0.0000 0.983 1.000 0.000
#> SRR1357389 1 0.0000 0.983 1.000 0.000
#> SRR1404227 2 0.0000 0.991 0.000 1.000
#> SRR1376830 1 0.0000 0.983 1.000 0.000
#> SRR1500661 1 0.0000 0.983 1.000 0.000
#> SRR1080294 2 0.0000 0.991 0.000 1.000
#> SRR1336314 2 0.0000 0.991 0.000 1.000
#> SRR1102152 1 0.0000 0.983 1.000 0.000
#> SRR1345244 2 0.0000 0.991 0.000 1.000
#> SRR1478637 2 0.0000 0.991 0.000 1.000
#> SRR1443776 2 0.0000 0.991 0.000 1.000
#> SRR1120939 2 0.0000 0.991 0.000 1.000
#> SRR1080117 2 0.0000 0.991 0.000 1.000
#> SRR1102899 2 0.0000 0.991 0.000 1.000
#> SRR1091865 1 0.0000 0.983 1.000 0.000
#> SRR1361072 1 0.0000 0.983 1.000 0.000
#> SRR1487890 1 0.0000 0.983 1.000 0.000
#> SRR1349456 2 0.0000 0.991 0.000 1.000
#> SRR1389384 1 0.5294 0.858 0.880 0.120
#> SRR1316096 2 0.0000 0.991 0.000 1.000
#> SRR1408512 1 0.0000 0.983 1.000 0.000
#> SRR1447547 2 0.0000 0.991 0.000 1.000
#> SRR1354053 2 0.0000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1499040 1 0.2796 0.894 0.908 0.000 0.092
#> SRR1322312 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1349479 2 0.0592 0.977 0.000 0.988 0.012
#> SRR1431248 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1405054 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.989 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1335236 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1095383 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1073435 2 0.0000 0.985 0.000 1.000 0.000
#> SRR659649 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1105248 2 0.1411 0.956 0.000 0.964 0.036
#> SRR1338257 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1414637 2 0.0237 0.982 0.004 0.996 0.000
#> SRR1478113 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1478789 3 0.1411 0.961 0.000 0.036 0.964
#> SRR1414185 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1338103 1 0.1411 0.954 0.964 0.036 0.000
#> SRR1472012 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1334866 2 0.0424 0.980 0.000 0.992 0.008
#> SRR1089446 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1383283 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1350239 3 0.2537 0.914 0.000 0.080 0.920
#> SRR1353878 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1413008 3 0.2537 0.914 0.000 0.080 0.920
#> SRR1407179 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1095913 2 0.0237 0.983 0.000 0.996 0.004
#> SRR1403544 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.989 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1436228 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1445218 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1082151 2 0.0592 0.975 0.012 0.988 0.000
#> SRR1349320 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1499675 1 0.7067 0.349 0.596 0.028 0.376
#> SRR1440467 3 0.0000 0.994 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1313807 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1470751 2 0.0747 0.971 0.016 0.984 0.000
#> SRR1403434 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1365313 2 0.4291 0.783 0.000 0.820 0.180
#> SRR1321877 3 0.0000 0.994 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1433476 2 0.1529 0.953 0.000 0.960 0.040
#> SRR1101883 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1341877 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1090556 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1478637 2 0.3267 0.872 0.000 0.884 0.116
#> SRR1443776 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1349456 2 0.4555 0.761 0.000 0.800 0.200
#> SRR1389384 1 0.2625 0.902 0.916 0.084 0.000
#> SRR1316096 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.989 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.985 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.985 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1499040 1 0.5166 0.7583 0.756 0.012 0.044 0.188
#> SRR1322312 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1100991 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1349479 4 0.4252 0.7912 0.000 0.252 0.004 0.744
#> SRR1431248 4 0.3610 0.5354 0.000 0.200 0.000 0.800
#> SRR1405054 1 0.2944 0.8426 0.868 0.000 0.128 0.004
#> SRR1312266 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1409790 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1352507 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1383763 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.4972 0.7815 0.000 0.456 0.000 0.544
#> SRR1473674 2 0.0469 0.7713 0.000 0.988 0.000 0.012
#> SRR1390499 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR821043 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1455653 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1335236 2 0.0592 0.7697 0.000 0.984 0.000 0.016
#> SRR1095383 4 0.4961 0.7912 0.000 0.448 0.000 0.552
#> SRR1479489 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.1022 0.7571 0.000 0.968 0.000 0.032
#> SRR1073435 4 0.4933 0.7903 0.000 0.432 0.000 0.568
#> SRR659649 3 0.0336 0.9333 0.000 0.000 0.992 0.008
#> SRR1395999 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.5018 0.8606 0.000 0.332 0.012 0.656
#> SRR1338257 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1499395 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1350002 2 0.0336 0.7725 0.000 0.992 0.000 0.008
#> SRR1489757 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1414637 2 0.3649 0.7244 0.000 0.796 0.000 0.204
#> SRR1478113 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1322477 1 0.4549 0.7752 0.776 0.036 0.000 0.188
#> SRR1478789 2 0.6545 0.5056 0.000 0.632 0.216 0.152
#> SRR1414185 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1069141 2 0.0921 0.7613 0.000 0.972 0.000 0.028
#> SRR1376852 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.4994 0.7377 0.744 0.048 0.000 0.208
#> SRR1472012 1 0.4137 0.7808 0.780 0.012 0.000 0.208
#> SRR1340325 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1488790 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.4781 0.6374 0.000 0.660 0.004 0.336
#> SRR1089446 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1344445 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1412969 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1071668 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1075804 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1383283 4 0.4933 0.7903 0.000 0.432 0.000 0.568
#> SRR1350239 4 0.4897 0.4958 0.000 0.008 0.332 0.660
#> SRR1353878 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1375721 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.3172 0.8371 0.840 0.000 0.000 0.160
#> SRR1090095 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1098737 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.4897 0.4958 0.000 0.008 0.332 0.660
#> SRR1407179 3 0.1792 0.9303 0.000 0.000 0.932 0.068
#> SRR1095913 2 0.1042 0.7651 0.000 0.972 0.008 0.020
#> SRR1403544 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1436228 2 0.3726 0.7223 0.000 0.788 0.000 0.212
#> SRR1445218 2 0.1022 0.7571 0.000 0.968 0.000 0.032
#> SRR1485438 2 0.3649 0.7244 0.000 0.796 0.000 0.204
#> SRR1358143 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1380806 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1087007 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1086256 2 0.0817 0.7739 0.000 0.976 0.000 0.024
#> SRR1346734 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1414515 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.4356 0.6573 0.000 0.708 0.000 0.292
#> SRR1349320 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1317554 4 0.4661 0.8657 0.000 0.348 0.000 0.652
#> SRR1076022 2 0.0707 0.7676 0.000 0.980 0.000 0.020
#> SRR1339573 3 0.2408 0.9354 0.000 0.000 0.896 0.104
#> SRR1455878 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.2281 0.9361 0.000 0.000 0.904 0.096
#> SRR1387397 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1499675 1 0.8785 0.0886 0.416 0.136 0.088 0.360
#> SRR1440467 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR807995 2 0.0188 0.7739 0.000 0.996 0.000 0.004
#> SRR1476485 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1388214 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1456051 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1444083 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1313807 4 0.4605 0.7173 0.000 0.336 0.000 0.664
#> SRR1470751 2 0.4356 0.6573 0.000 0.708 0.000 0.292
#> SRR1403434 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1390540 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0707 0.7676 0.000 0.980 0.000 0.020
#> SRR1325290 1 0.4253 0.7776 0.776 0.016 0.000 0.208
#> SRR1070689 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.4919 0.6643 0.000 0.752 0.048 0.200
#> SRR1321877 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR815711 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1433476 4 0.3908 0.7455 0.000 0.212 0.004 0.784
#> SRR1101883 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1433729 4 0.4961 0.7912 0.000 0.448 0.000 0.552
#> SRR1341877 1 0.3351 0.8434 0.844 0.008 0.000 0.148
#> SRR1090556 1 0.1474 0.9251 0.948 0.000 0.000 0.052
#> SRR1357389 3 0.0188 0.9321 0.000 0.000 0.996 0.004
#> SRR1404227 3 0.5293 0.8086 0.000 0.100 0.748 0.152
#> SRR1376830 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.4961 0.7912 0.000 0.448 0.000 0.552
#> SRR1336314 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1102152 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1345244 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1478637 2 0.3870 0.7221 0.000 0.788 0.004 0.208
#> SRR1443776 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1120939 3 0.2281 0.9361 0.000 0.000 0.904 0.096
#> SRR1080117 3 0.2760 0.9323 0.000 0.000 0.872 0.128
#> SRR1102899 2 0.1022 0.7571 0.000 0.968 0.000 0.032
#> SRR1091865 1 0.3695 0.8277 0.828 0.016 0.000 0.156
#> SRR1361072 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9583 1.000 0.000 0.000 0.000
#> SRR1349456 2 0.4197 0.6745 0.000 0.808 0.036 0.156
#> SRR1389384 2 0.7159 0.4510 0.260 0.552 0.000 0.188
#> SRR1316096 2 0.0921 0.7613 0.000 0.972 0.000 0.028
#> SRR1408512 1 0.0188 0.9567 0.996 0.000 0.000 0.004
#> SRR1447547 4 0.4643 0.8670 0.000 0.344 0.000 0.656
#> SRR1354053 4 0.4661 0.8657 0.000 0.348 0.000 0.652
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0162 0.9344 0.996 0.000 0.004 0.000 0.000
#> SRR1349562 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1499040 5 0.5234 0.2635 0.436 0.036 0.004 0.000 0.524
#> SRR1322312 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1100991 2 0.0162 0.9019 0.000 0.996 0.004 0.000 0.000
#> SRR1349479 4 0.2127 0.6363 0.000 0.000 0.108 0.892 0.000
#> SRR1431248 5 0.2623 0.6177 0.004 0.000 0.016 0.096 0.884
#> SRR1405054 2 0.4173 0.3922 0.300 0.688 0.012 0.000 0.000
#> SRR1312266 1 0.0865 0.9235 0.972 0.000 0.004 0.000 0.024
#> SRR1409790 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1352507 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1383763 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1468314 4 0.2612 0.7029 0.000 0.000 0.124 0.868 0.008
#> SRR1473674 4 0.6701 0.4246 0.000 0.000 0.272 0.428 0.300
#> SRR1390499 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1455653 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1335236 4 0.6706 0.4283 0.000 0.000 0.284 0.428 0.288
#> SRR1095383 4 0.2280 0.7053 0.000 0.000 0.120 0.880 0.000
#> SRR1479489 1 0.0404 0.9321 0.988 0.000 0.012 0.000 0.000
#> SRR1310433 4 0.6638 0.4521 0.000 0.000 0.272 0.452 0.276
#> SRR1073435 4 0.3975 0.6736 0.000 0.000 0.144 0.792 0.064
#> SRR659649 3 0.4307 0.4401 0.000 0.496 0.504 0.000 0.000
#> SRR1395999 1 0.0290 0.9335 0.992 0.000 0.008 0.000 0.000
#> SRR1105248 4 0.0955 0.6906 0.000 0.004 0.028 0.968 0.000
#> SRR1338257 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1499395 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1350002 4 0.6718 0.4122 0.000 0.000 0.272 0.420 0.308
#> SRR1489757 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1414637 5 0.3704 0.6136 0.000 0.000 0.092 0.088 0.820
#> SRR1478113 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1322477 5 0.4288 0.4046 0.384 0.000 0.004 0.000 0.612
#> SRR1478789 3 0.0451 0.5256 0.000 0.004 0.988 0.000 0.008
#> SRR1414185 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1069141 4 0.6672 0.4404 0.000 0.000 0.272 0.440 0.288
#> SRR1376852 1 0.2648 0.7937 0.848 0.000 0.000 0.000 0.152
#> SRR1323491 1 0.0162 0.9344 0.996 0.000 0.004 0.000 0.000
#> SRR1338103 5 0.4736 0.4902 0.312 0.000 0.028 0.004 0.656
#> SRR1472012 5 0.4329 0.4938 0.312 0.000 0.016 0.000 0.672
#> SRR1340325 1 0.0404 0.9321 0.988 0.000 0.012 0.000 0.000
#> SRR1087321 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1488790 1 0.0162 0.9344 0.996 0.000 0.004 0.000 0.000
#> SRR1334866 5 0.2966 0.6295 0.000 0.000 0.184 0.000 0.816
#> SRR1089446 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1344445 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1412969 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1071668 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1075804 1 0.1121 0.9064 0.956 0.000 0.000 0.000 0.044
#> SRR1383283 4 0.3386 0.6943 0.000 0.000 0.128 0.832 0.040
#> SRR1350239 4 0.4273 -0.0128 0.000 0.448 0.000 0.552 0.000
#> SRR1353878 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1375721 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.4552 -0.0536 0.524 0.000 0.008 0.000 0.468
#> SRR1090095 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1098737 1 0.1121 0.9064 0.956 0.000 0.000 0.000 0.044
#> SRR1349409 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.4273 -0.0128 0.000 0.448 0.000 0.552 0.000
#> SRR1407179 2 0.6631 0.0164 0.000 0.440 0.324 0.000 0.236
#> SRR1095913 3 0.6271 -0.4985 0.000 0.000 0.440 0.412 0.148
#> SRR1403544 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0290 0.9335 0.992 0.000 0.008 0.000 0.000
#> SRR807971 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1436228 5 0.2270 0.6394 0.000 0.000 0.076 0.020 0.904
#> SRR1445218 4 0.6638 0.4521 0.000 0.000 0.272 0.452 0.276
#> SRR1485438 5 0.4786 0.5299 0.000 0.000 0.188 0.092 0.720
#> SRR1358143 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1380806 1 0.0162 0.9341 0.996 0.000 0.004 0.000 0.000
#> SRR1379426 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1087007 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1086256 4 0.6686 0.4202 0.000 0.000 0.256 0.428 0.316
#> SRR1346734 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.4334 0.5880 0.000 0.000 0.140 0.092 0.768
#> SRR1349320 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1317554 4 0.1608 0.7097 0.000 0.000 0.072 0.928 0.000
#> SRR1076022 4 0.6672 0.4404 0.000 0.000 0.272 0.440 0.288
#> SRR1339573 3 0.3999 0.7288 0.000 0.344 0.656 0.000 0.000
#> SRR1455878 1 0.1877 0.8902 0.924 0.000 0.012 0.000 0.064
#> SRR1446203 3 0.4524 0.7248 0.000 0.336 0.644 0.000 0.020
#> SRR1387397 1 0.3403 0.7726 0.820 0.008 0.012 0.000 0.160
#> SRR1402590 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.1331 0.9093 0.952 0.000 0.008 0.000 0.040
#> SRR1331488 1 0.0162 0.9344 0.996 0.000 0.004 0.000 0.000
#> SRR1499675 3 0.6948 -0.1875 0.244 0.000 0.384 0.008 0.364
#> SRR1440467 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR807995 4 0.6758 0.3609 0.000 0.000 0.272 0.392 0.336
#> SRR1476485 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1388214 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1456051 1 0.0290 0.9335 0.992 0.000 0.008 0.000 0.000
#> SRR1473275 2 0.0703 0.8760 0.000 0.976 0.024 0.000 0.000
#> SRR1444083 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1313807 4 0.3574 0.6825 0.000 0.000 0.168 0.804 0.028
#> SRR1470751 5 0.4334 0.5880 0.000 0.000 0.140 0.092 0.768
#> SRR1403434 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1390540 1 0.0162 0.9344 0.996 0.000 0.004 0.000 0.000
#> SRR1093861 4 0.6672 0.4404 0.000 0.000 0.272 0.440 0.288
#> SRR1325290 5 0.4066 0.4752 0.324 0.000 0.004 0.000 0.672
#> SRR1070689 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.4009 0.0805 0.000 0.000 0.684 0.004 0.312
#> SRR1321877 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR815711 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1433476 4 0.3039 0.5511 0.000 0.000 0.192 0.808 0.000
#> SRR1101883 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1433729 4 0.2280 0.7053 0.000 0.000 0.120 0.880 0.000
#> SRR1341877 1 0.5852 0.2279 0.556 0.000 0.116 0.000 0.328
#> SRR1090556 1 0.4851 0.4171 0.624 0.000 0.036 0.000 0.340
#> SRR1357389 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> SRR1404227 3 0.5038 0.5876 0.000 0.132 0.704 0.000 0.164
#> SRR1376830 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1080294 4 0.2280 0.7053 0.000 0.000 0.120 0.880 0.000
#> SRR1336314 4 0.0000 0.7051 0.000 0.000 0.000 1.000 0.000
#> SRR1102152 1 0.1106 0.9207 0.964 0.000 0.012 0.000 0.024
#> SRR1345244 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1478637 5 0.2676 0.6460 0.000 0.000 0.080 0.036 0.884
#> SRR1443776 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1120939 3 0.4524 0.7248 0.000 0.336 0.644 0.000 0.020
#> SRR1080117 3 0.3857 0.7599 0.000 0.312 0.688 0.000 0.000
#> SRR1102899 4 0.6638 0.4521 0.000 0.000 0.272 0.452 0.276
#> SRR1091865 1 0.4644 -0.0385 0.528 0.000 0.012 0.000 0.460
#> SRR1361072 1 0.0404 0.9321 0.988 0.000 0.012 0.000 0.000
#> SRR1487890 1 0.0000 0.9350 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.1124 0.5064 0.000 0.000 0.960 0.004 0.036
#> SRR1389384 5 0.4113 0.6434 0.076 0.000 0.140 0.000 0.784
#> SRR1316096 4 0.6661 0.4445 0.000 0.000 0.272 0.444 0.284
#> SRR1408512 1 0.2843 0.8119 0.848 0.000 0.008 0.000 0.144
#> SRR1447547 4 0.0290 0.7013 0.000 0.000 0.000 0.992 0.008
#> SRR1354053 4 0.1851 0.7092 0.000 0.000 0.088 0.912 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0458 0.9015 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1349562 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.7339 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1499040 5 0.7425 0.3931 0.264 0.268 0.124 0.000 0.344 0.000
#> SRR1322312 1 0.0146 0.9029 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324412 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1100991 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1349479 4 0.2738 0.5945 0.000 0.004 0.000 0.820 0.000 0.176
#> SRR1431248 5 0.4243 0.5634 0.000 0.104 0.028 0.096 0.772 0.000
#> SRR1405054 3 0.1910 0.7913 0.108 0.000 0.892 0.000 0.000 0.000
#> SRR1312266 1 0.2122 0.8668 0.912 0.040 0.008 0.000 0.040 0.000
#> SRR1409790 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1352507 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1383763 1 0.0790 0.8883 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR1468314 4 0.3717 0.4100 0.000 0.384 0.000 0.616 0.000 0.000
#> SRR1473674 2 0.3290 0.5921 0.000 0.744 0.004 0.252 0.000 0.000
#> SRR1390499 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.1007 0.7232 0.000 0.044 0.000 0.956 0.000 0.000
#> SRR1455653 4 0.1267 0.7171 0.000 0.060 0.000 0.940 0.000 0.000
#> SRR1335236 2 0.3309 0.5827 0.000 0.720 0.000 0.280 0.000 0.000
#> SRR1095383 4 0.3563 0.5037 0.000 0.336 0.000 0.664 0.000 0.000
#> SRR1479489 1 0.0363 0.9030 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1310433 2 0.3351 0.5751 0.000 0.712 0.000 0.288 0.000 0.000
#> SRR1073435 4 0.5871 0.3515 0.000 0.312 0.000 0.468 0.220 0.000
#> SRR659649 6 0.2664 0.7319 0.000 0.000 0.184 0.000 0.000 0.816
#> SRR1395999 1 0.0508 0.9022 0.984 0.000 0.004 0.000 0.012 0.000
#> SRR1105248 4 0.0551 0.7287 0.000 0.004 0.000 0.984 0.004 0.008
#> SRR1338257 1 0.2122 0.8660 0.912 0.040 0.008 0.000 0.040 0.000
#> SRR1499395 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1350002 2 0.3678 0.5925 0.000 0.748 0.016 0.228 0.008 0.000
#> SRR1489757 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1414637 2 0.5638 -0.0157 0.000 0.548 0.096 0.024 0.332 0.000
#> SRR1478113 4 0.0291 0.7318 0.000 0.004 0.000 0.992 0.004 0.000
#> SRR1322477 5 0.7354 0.3970 0.236 0.304 0.096 0.004 0.360 0.000
#> SRR1478789 6 0.1958 0.8237 0.000 0.100 0.000 0.000 0.004 0.896
#> SRR1414185 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1069141 2 0.3351 0.5751 0.000 0.712 0.000 0.288 0.000 0.000
#> SRR1376852 1 0.3672 0.3686 0.632 0.000 0.000 0.000 0.368 0.000
#> SRR1323491 1 0.0458 0.9015 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1338103 5 0.1265 0.6183 0.044 0.008 0.000 0.000 0.948 0.000
#> SRR1472012 5 0.3206 0.6155 0.048 0.108 0.008 0.000 0.836 0.000
#> SRR1340325 1 0.0777 0.8984 0.972 0.000 0.004 0.000 0.024 0.000
#> SRR1087321 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1334866 2 0.6003 -0.2385 0.000 0.444 0.064 0.000 0.428 0.064
#> SRR1089446 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1344445 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1412969 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1071668 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1075804 1 0.2933 0.7095 0.796 0.000 0.004 0.000 0.200 0.000
#> SRR1383283 4 0.4775 0.4609 0.000 0.348 0.000 0.588 0.064 0.000
#> SRR1350239 4 0.3830 0.4282 0.000 0.008 0.280 0.704 0.004 0.004
#> SRR1353878 1 0.2122 0.8660 0.912 0.040 0.008 0.000 0.040 0.000
#> SRR1375721 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.6698 -0.3716 0.396 0.156 0.064 0.000 0.384 0.000
#> SRR1090095 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0291 0.7318 0.000 0.004 0.000 0.992 0.004 0.000
#> SRR1098737 1 0.2964 0.7131 0.792 0.000 0.004 0.000 0.204 0.000
#> SRR1349409 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.3830 0.4282 0.000 0.008 0.280 0.704 0.004 0.004
#> SRR1407179 5 0.4002 0.3999 0.000 0.000 0.260 0.000 0.704 0.036
#> SRR1095913 2 0.5539 0.3679 0.000 0.548 0.000 0.272 0.000 0.180
#> SRR1403544 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0858 0.8971 0.968 0.000 0.004 0.000 0.028 0.000
#> SRR807971 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1436228 5 0.3420 0.4508 0.000 0.240 0.012 0.000 0.748 0.000
#> SRR1445218 2 0.3351 0.5751 0.000 0.712 0.000 0.288 0.000 0.000
#> SRR1485438 2 0.4832 0.2379 0.000 0.696 0.080 0.024 0.200 0.000
#> SRR1358143 1 0.0146 0.9029 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1328760 1 0.2051 0.8689 0.916 0.036 0.008 0.000 0.040 0.000
#> SRR1380806 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1379426 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1087007 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1086256 2 0.3773 0.5853 0.000 0.752 0.000 0.204 0.044 0.000
#> SRR1346734 4 0.0000 0.7339 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.5617 0.0712 0.000 0.628 0.104 0.048 0.220 0.000
#> SRR1349320 4 0.0000 0.7339 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1317554 4 0.3309 0.5669 0.000 0.280 0.000 0.720 0.000 0.000
#> SRR1076022 2 0.3309 0.5827 0.000 0.720 0.000 0.280 0.000 0.000
#> SRR1339573 6 0.0865 0.9057 0.000 0.000 0.036 0.000 0.000 0.964
#> SRR1455878 1 0.4074 0.5004 0.656 0.016 0.004 0.000 0.324 0.000
#> SRR1446203 6 0.2003 0.8819 0.000 0.000 0.044 0.000 0.044 0.912
#> SRR1387397 5 0.4537 -0.0474 0.484 0.004 0.024 0.000 0.488 0.000
#> SRR1402590 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.2738 0.7560 0.820 0.000 0.004 0.000 0.176 0.000
#> SRR1331488 1 0.0547 0.9010 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1499675 5 0.3459 0.5725 0.044 0.008 0.000 0.004 0.820 0.124
#> SRR1440467 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR807995 2 0.3678 0.5925 0.000 0.748 0.016 0.228 0.008 0.000
#> SRR1476485 4 0.0000 0.7339 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.2190 0.8644 0.908 0.040 0.008 0.000 0.044 0.000
#> SRR1456051 1 0.0692 0.8998 0.976 0.000 0.004 0.000 0.020 0.000
#> SRR1473275 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1444083 1 0.2122 0.8660 0.912 0.040 0.008 0.000 0.040 0.000
#> SRR1313807 4 0.4765 0.4665 0.000 0.352 0.000 0.592 0.052 0.004
#> SRR1470751 2 0.5773 0.0443 0.000 0.612 0.104 0.056 0.228 0.000
#> SRR1403434 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1390540 1 0.0458 0.9015 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1093861 2 0.3309 0.5827 0.000 0.720 0.000 0.280 0.000 0.000
#> SRR1325290 5 0.4574 0.5704 0.036 0.172 0.060 0.000 0.732 0.000
#> SRR1070689 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0632 0.8935 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1081184 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 2 0.5834 0.1024 0.000 0.468 0.000 0.000 0.328 0.204
#> SRR1321877 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1433476 4 0.2838 0.5875 0.000 0.004 0.000 0.808 0.000 0.188
#> SRR1101883 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1433729 4 0.3578 0.4979 0.000 0.340 0.000 0.660 0.000 0.000
#> SRR1341877 5 0.3210 0.5968 0.168 0.000 0.000 0.000 0.804 0.028
#> SRR1090556 5 0.2805 0.5808 0.184 0.000 0.004 0.000 0.812 0.000
#> SRR1357389 3 0.1910 0.9852 0.000 0.000 0.892 0.000 0.000 0.108
#> SRR1404227 6 0.4493 0.4394 0.000 0.040 0.000 0.000 0.364 0.596
#> SRR1376830 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0363 0.8996 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1080294 4 0.3578 0.4979 0.000 0.340 0.000 0.660 0.000 0.000
#> SRR1336314 4 0.0000 0.7339 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1102152 1 0.2009 0.8677 0.916 0.040 0.004 0.000 0.040 0.000
#> SRR1345244 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1478637 5 0.5497 0.2212 0.000 0.392 0.092 0.012 0.504 0.000
#> SRR1443776 6 0.0000 0.9272 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 6 0.2070 0.8795 0.000 0.000 0.044 0.000 0.048 0.908
#> SRR1080117 6 0.0146 0.9270 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1102899 2 0.3371 0.5690 0.000 0.708 0.000 0.292 0.000 0.000
#> SRR1091865 1 0.7257 -0.2836 0.384 0.264 0.104 0.000 0.248 0.000
#> SRR1361072 1 0.0935 0.8956 0.964 0.000 0.004 0.000 0.032 0.000
#> SRR1487890 1 0.0000 0.9043 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.4145 0.6430 0.000 0.220 0.000 0.004 0.052 0.724
#> SRR1389384 2 0.5390 -0.0157 0.020 0.612 0.104 0.000 0.264 0.000
#> SRR1316096 2 0.3330 0.5791 0.000 0.716 0.000 0.284 0.000 0.000
#> SRR1408512 1 0.4426 0.3754 0.596 0.020 0.008 0.000 0.376 0.000
#> SRR1447547 4 0.0508 0.7279 0.000 0.012 0.000 0.984 0.004 0.000
#> SRR1354053 4 0.3446 0.5379 0.000 0.308 0.000 0.692 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.949 0.946 0.969 0.4923 0.503 0.503
#> 3 3 0.879 0.909 0.961 0.3032 0.737 0.532
#> 4 4 0.733 0.734 0.869 0.1543 0.898 0.717
#> 5 5 0.703 0.576 0.784 0.0488 0.938 0.776
#> 6 6 0.822 0.772 0.893 0.0531 0.874 0.527
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.978 1.000 0.000
#> SRR1349562 1 0.0000 0.978 1.000 0.000
#> SRR1353376 2 0.0000 0.959 0.000 1.000
#> SRR1499040 1 0.1414 0.963 0.980 0.020
#> SRR1322312 1 0.0000 0.978 1.000 0.000
#> SRR1324412 1 0.8386 0.629 0.732 0.268
#> SRR1100991 1 0.3584 0.920 0.932 0.068
#> SRR1349479 2 0.0000 0.959 0.000 1.000
#> SRR1431248 2 0.9977 0.152 0.472 0.528
#> SRR1405054 1 0.0000 0.978 1.000 0.000
#> SRR1312266 1 0.0000 0.978 1.000 0.000
#> SRR1409790 2 0.5294 0.900 0.120 0.880
#> SRR1352507 2 0.3733 0.941 0.072 0.928
#> SRR1383763 1 0.0000 0.978 1.000 0.000
#> SRR1468314 2 0.0000 0.959 0.000 1.000
#> SRR1473674 2 0.0000 0.959 0.000 1.000
#> SRR1390499 1 0.0000 0.978 1.000 0.000
#> SRR821043 2 0.0000 0.959 0.000 1.000
#> SRR1455653 2 0.0000 0.959 0.000 1.000
#> SRR1335236 2 0.0000 0.959 0.000 1.000
#> SRR1095383 2 0.0000 0.959 0.000 1.000
#> SRR1479489 1 0.0000 0.978 1.000 0.000
#> SRR1310433 2 0.0000 0.959 0.000 1.000
#> SRR1073435 2 0.0000 0.959 0.000 1.000
#> SRR659649 2 0.3733 0.941 0.072 0.928
#> SRR1395999 1 0.0000 0.978 1.000 0.000
#> SRR1105248 2 0.0000 0.959 0.000 1.000
#> SRR1338257 1 0.0000 0.978 1.000 0.000
#> SRR1499395 2 0.3733 0.941 0.072 0.928
#> SRR1350002 2 0.0000 0.959 0.000 1.000
#> SRR1489757 2 0.4562 0.923 0.096 0.904
#> SRR1414637 1 0.7299 0.749 0.796 0.204
#> SRR1478113 2 0.2948 0.931 0.052 0.948
#> SRR1322477 1 0.1633 0.960 0.976 0.024
#> SRR1478789 2 0.0000 0.959 0.000 1.000
#> SRR1414185 2 0.3733 0.941 0.072 0.928
#> SRR1069141 2 0.0000 0.959 0.000 1.000
#> SRR1376852 1 0.0000 0.978 1.000 0.000
#> SRR1323491 1 0.0000 0.978 1.000 0.000
#> SRR1338103 1 0.6343 0.809 0.840 0.160
#> SRR1472012 1 0.0672 0.972 0.992 0.008
#> SRR1340325 1 0.0000 0.978 1.000 0.000
#> SRR1087321 2 0.0000 0.959 0.000 1.000
#> SRR1488790 1 0.0000 0.978 1.000 0.000
#> SRR1334866 2 0.3733 0.941 0.072 0.928
#> SRR1089446 2 0.3733 0.941 0.072 0.928
#> SRR1344445 2 0.3733 0.941 0.072 0.928
#> SRR1412969 2 0.2236 0.952 0.036 0.964
#> SRR1071668 2 0.4298 0.930 0.088 0.912
#> SRR1075804 1 0.0000 0.978 1.000 0.000
#> SRR1383283 2 0.0000 0.959 0.000 1.000
#> SRR1350239 2 0.3733 0.941 0.072 0.928
#> SRR1353878 1 0.0000 0.978 1.000 0.000
#> SRR1375721 1 0.0000 0.978 1.000 0.000
#> SRR1083983 1 0.0000 0.978 1.000 0.000
#> SRR1090095 1 0.0000 0.978 1.000 0.000
#> SRR1414792 1 0.0000 0.978 1.000 0.000
#> SRR1075102 2 0.0000 0.959 0.000 1.000
#> SRR1098737 1 0.0000 0.978 1.000 0.000
#> SRR1349409 1 0.0000 0.978 1.000 0.000
#> SRR1413008 2 0.3733 0.941 0.072 0.928
#> SRR1407179 2 0.3879 0.939 0.076 0.924
#> SRR1095913 2 0.0000 0.959 0.000 1.000
#> SRR1403544 1 0.0000 0.978 1.000 0.000
#> SRR1490546 1 0.0000 0.978 1.000 0.000
#> SRR807971 2 0.3733 0.941 0.072 0.928
#> SRR1436228 2 0.7139 0.800 0.196 0.804
#> SRR1445218 2 0.0000 0.959 0.000 1.000
#> SRR1485438 2 0.0938 0.955 0.012 0.988
#> SRR1358143 1 0.0000 0.978 1.000 0.000
#> SRR1328760 1 0.0000 0.978 1.000 0.000
#> SRR1380806 1 0.0000 0.978 1.000 0.000
#> SRR1379426 2 0.3733 0.941 0.072 0.928
#> SRR1087007 2 0.0000 0.959 0.000 1.000
#> SRR1086256 2 0.0000 0.959 0.000 1.000
#> SRR1346734 2 0.0000 0.959 0.000 1.000
#> SRR1414515 1 0.0000 0.978 1.000 0.000
#> SRR1082151 1 0.6247 0.820 0.844 0.156
#> SRR1349320 2 0.0000 0.959 0.000 1.000
#> SRR1317554 2 0.0000 0.959 0.000 1.000
#> SRR1076022 2 0.0000 0.959 0.000 1.000
#> SRR1339573 2 0.3733 0.941 0.072 0.928
#> SRR1455878 1 0.0000 0.978 1.000 0.000
#> SRR1446203 2 0.0000 0.959 0.000 1.000
#> SRR1387397 1 0.0000 0.978 1.000 0.000
#> SRR1402590 1 0.0000 0.978 1.000 0.000
#> SRR1317532 1 0.0000 0.978 1.000 0.000
#> SRR1331488 1 0.0376 0.975 0.996 0.004
#> SRR1499675 2 0.3733 0.941 0.072 0.928
#> SRR1440467 2 0.3733 0.941 0.072 0.928
#> SRR807995 2 0.0000 0.959 0.000 1.000
#> SRR1476485 2 0.0000 0.959 0.000 1.000
#> SRR1388214 1 0.0000 0.978 1.000 0.000
#> SRR1456051 1 0.0000 0.978 1.000 0.000
#> SRR1473275 2 0.4562 0.923 0.096 0.904
#> SRR1444083 1 0.0000 0.978 1.000 0.000
#> SRR1313807 2 0.0000 0.959 0.000 1.000
#> SRR1470751 1 0.5294 0.864 0.880 0.120
#> SRR1403434 2 0.3584 0.943 0.068 0.932
#> SRR1390540 1 0.0000 0.978 1.000 0.000
#> SRR1093861 2 0.0000 0.959 0.000 1.000
#> SRR1325290 1 0.0672 0.973 0.992 0.008
#> SRR1070689 1 0.0000 0.978 1.000 0.000
#> SRR1384049 1 0.0000 0.978 1.000 0.000
#> SRR1081184 1 0.0000 0.978 1.000 0.000
#> SRR1324295 1 0.0000 0.978 1.000 0.000
#> SRR1365313 2 0.0000 0.959 0.000 1.000
#> SRR1321877 2 0.3733 0.941 0.072 0.928
#> SRR815711 2 0.3733 0.941 0.072 0.928
#> SRR1433476 2 0.3431 0.944 0.064 0.936
#> SRR1101883 2 0.3733 0.941 0.072 0.928
#> SRR1433729 2 0.0000 0.959 0.000 1.000
#> SRR1341877 2 0.5059 0.908 0.112 0.888
#> SRR1090556 1 0.3114 0.932 0.944 0.056
#> SRR1357389 2 0.3733 0.941 0.072 0.928
#> SRR1404227 2 0.0000 0.959 0.000 1.000
#> SRR1376830 1 0.0000 0.978 1.000 0.000
#> SRR1500661 1 0.0000 0.978 1.000 0.000
#> SRR1080294 2 0.0000 0.959 0.000 1.000
#> SRR1336314 2 0.0000 0.959 0.000 1.000
#> SRR1102152 1 0.0000 0.978 1.000 0.000
#> SRR1345244 2 0.3733 0.941 0.072 0.928
#> SRR1478637 2 0.1843 0.954 0.028 0.972
#> SRR1443776 2 0.0000 0.959 0.000 1.000
#> SRR1120939 2 0.0000 0.959 0.000 1.000
#> SRR1080117 2 0.0000 0.959 0.000 1.000
#> SRR1102899 2 0.0000 0.959 0.000 1.000
#> SRR1091865 1 0.0000 0.978 1.000 0.000
#> SRR1361072 1 0.0000 0.978 1.000 0.000
#> SRR1487890 1 0.0000 0.978 1.000 0.000
#> SRR1349456 2 0.0000 0.959 0.000 1.000
#> SRR1389384 1 0.5946 0.835 0.856 0.144
#> SRR1316096 2 0.0000 0.959 0.000 1.000
#> SRR1408512 1 0.0376 0.975 0.996 0.004
#> SRR1447547 2 0.3733 0.941 0.072 0.928
#> SRR1354053 2 0.0000 0.959 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1353376 2 0.1289 0.922 0.000 0.968 0.032
#> SRR1499040 1 0.5465 0.597 0.712 0.000 0.288
#> SRR1322312 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1349479 2 0.6126 0.340 0.000 0.600 0.400
#> SRR1431248 3 0.1015 0.927 0.012 0.008 0.980
#> SRR1405054 3 0.4887 0.729 0.228 0.000 0.772
#> SRR1312266 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.981 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1335236 2 0.4702 0.742 0.000 0.788 0.212
#> SRR1095383 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1073435 3 0.0424 0.933 0.000 0.008 0.992
#> SRR659649 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1395999 1 0.1529 0.941 0.960 0.000 0.040
#> SRR1105248 3 0.5178 0.645 0.000 0.256 0.744
#> SRR1338257 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1414637 3 0.4796 0.736 0.220 0.000 0.780
#> SRR1478113 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1478789 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1338103 3 0.4796 0.736 0.220 0.000 0.780
#> SRR1472012 3 0.6274 0.246 0.456 0.000 0.544
#> SRR1340325 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1334866 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1089446 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1383283 3 0.1411 0.912 0.000 0.036 0.964
#> SRR1350239 3 0.0424 0.933 0.000 0.008 0.992
#> SRR1353878 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1075102 2 0.1964 0.903 0.000 0.944 0.056
#> SRR1098737 1 0.0892 0.962 0.980 0.000 0.020
#> SRR1349409 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1413008 3 0.0424 0.933 0.000 0.008 0.992
#> SRR1407179 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1095913 3 0.0424 0.933 0.000 0.008 0.992
#> SRR1403544 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.981 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1436228 3 0.0475 0.933 0.004 0.004 0.992
#> SRR1445218 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1485438 3 0.0892 0.922 0.000 0.020 0.980
#> SRR1358143 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1086256 2 0.3752 0.816 0.000 0.856 0.144
#> SRR1346734 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1082151 1 0.1163 0.954 0.972 0.000 0.028
#> SRR1349320 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1455878 1 0.2448 0.900 0.924 0.000 0.076
#> SRR1446203 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1387397 3 0.5327 0.671 0.272 0.000 0.728
#> SRR1402590 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1317532 3 0.5327 0.671 0.272 0.000 0.728
#> SRR1331488 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1499675 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1440467 3 0.0000 0.936 0.000 0.000 1.000
#> SRR807995 2 0.4452 0.768 0.000 0.808 0.192
#> SRR1476485 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1444083 3 0.5968 0.500 0.364 0.000 0.636
#> SRR1313807 3 0.0424 0.933 0.000 0.008 0.992
#> SRR1470751 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1403434 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1325290 3 0.5431 0.652 0.284 0.000 0.716
#> SRR1070689 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1365313 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1321877 3 0.0000 0.936 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1433476 3 0.0892 0.925 0.000 0.020 0.980
#> SRR1101883 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1433729 2 0.6204 0.258 0.000 0.576 0.424
#> SRR1341877 3 0.3752 0.812 0.144 0.000 0.856
#> SRR1090556 3 0.4796 0.736 0.220 0.000 0.780
#> SRR1357389 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1478637 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1443776 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.936 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.981 1.000 0.000 0.000
#> SRR1349456 3 0.1289 0.912 0.000 0.032 0.968
#> SRR1389384 1 0.5016 0.680 0.760 0.000 0.240
#> SRR1316096 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1408512 1 0.2625 0.891 0.916 0.000 0.084
#> SRR1447547 3 0.0661 0.931 0.004 0.008 0.988
#> SRR1354053 2 0.0000 0.943 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1353376 2 0.4874 0.7172 0.000 0.764 0.180 0.056
#> SRR1499040 1 0.5769 0.3611 0.588 0.000 0.376 0.036
#> SRR1322312 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1324412 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1100991 4 0.4817 0.5322 0.000 0.000 0.388 0.612
#> SRR1349479 3 0.4164 0.5226 0.000 0.264 0.736 0.000
#> SRR1431248 4 0.0000 0.6723 0.000 0.000 0.000 1.000
#> SRR1405054 4 0.4719 0.5977 0.180 0.000 0.048 0.772
#> SRR1312266 1 0.0592 0.8858 0.984 0.000 0.000 0.016
#> SRR1409790 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1352507 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1383763 1 0.0817 0.8818 0.976 0.000 0.000 0.024
#> SRR1468314 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1473674 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR821043 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1455653 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.4877 0.3334 0.000 0.592 0.408 0.000
#> SRR1095383 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1479489 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1073435 4 0.0707 0.6763 0.000 0.000 0.020 0.980
#> SRR659649 3 0.4431 0.3324 0.000 0.000 0.696 0.304
#> SRR1395999 1 0.2149 0.8550 0.912 0.000 0.000 0.088
#> SRR1105248 4 0.4585 0.3592 0.000 0.000 0.332 0.668
#> SRR1338257 1 0.1557 0.8692 0.944 0.000 0.000 0.056
#> SRR1499395 3 0.0336 0.8631 0.000 0.000 0.992 0.008
#> SRR1350002 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1489757 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1414637 4 0.1510 0.6575 0.016 0.000 0.028 0.956
#> SRR1478113 2 0.2313 0.8793 0.000 0.924 0.044 0.032
#> SRR1322477 1 0.4804 0.5842 0.616 0.000 0.000 0.384
#> SRR1478789 3 0.0921 0.8607 0.000 0.000 0.972 0.028
#> SRR1414185 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1376852 1 0.3610 0.7762 0.800 0.000 0.000 0.200
#> SRR1323491 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1338103 4 0.0000 0.6723 0.000 0.000 0.000 1.000
#> SRR1472012 4 0.4456 0.3093 0.280 0.000 0.004 0.716
#> SRR1340325 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1334866 3 0.1867 0.8259 0.000 0.000 0.928 0.072
#> SRR1089446 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1344445 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1412969 3 0.0707 0.8638 0.000 0.000 0.980 0.020
#> SRR1071668 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1075804 1 0.4522 0.6627 0.680 0.000 0.000 0.320
#> SRR1383283 3 0.4985 0.1544 0.000 0.000 0.532 0.468
#> SRR1350239 4 0.1211 0.6774 0.000 0.000 0.040 0.960
#> SRR1353878 1 0.1022 0.8798 0.968 0.000 0.000 0.032
#> SRR1375721 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.4250 0.7031 0.724 0.000 0.000 0.276
#> SRR1090095 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1075102 2 0.4415 0.7803 0.000 0.804 0.056 0.140
#> SRR1098737 1 0.4790 0.5875 0.620 0.000 0.000 0.380
#> SRR1349409 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.1716 0.6745 0.000 0.000 0.064 0.936
#> SRR1407179 4 0.0921 0.6776 0.000 0.000 0.028 0.972
#> SRR1095913 3 0.4804 0.1145 0.000 0.000 0.616 0.384
#> SRR1403544 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0336 0.8880 0.992 0.000 0.000 0.008
#> SRR807971 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1436228 4 0.0592 0.6760 0.000 0.000 0.016 0.984
#> SRR1445218 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1485438 4 0.5632 0.2207 0.000 0.036 0.340 0.624
#> SRR1358143 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.3074 0.8146 0.848 0.000 0.000 0.152
#> SRR1380806 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0817 0.8624 0.000 0.000 0.976 0.024
#> SRR1087007 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1086256 4 0.7883 -0.0579 0.000 0.300 0.316 0.384
#> SRR1346734 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1082151 1 0.6179 0.5028 0.552 0.000 0.056 0.392
#> SRR1349320 2 0.0707 0.9150 0.000 0.980 0.000 0.020
#> SRR1317554 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1076022 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1339573 3 0.1716 0.8077 0.000 0.000 0.936 0.064
#> SRR1455878 1 0.4961 0.4570 0.552 0.000 0.000 0.448
#> SRR1446203 4 0.4955 0.4350 0.000 0.000 0.444 0.556
#> SRR1387397 4 0.0921 0.6768 0.000 0.000 0.028 0.972
#> SRR1402590 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1317532 4 0.0804 0.6753 0.008 0.000 0.012 0.980
#> SRR1331488 1 0.2868 0.8270 0.864 0.000 0.000 0.136
#> SRR1499675 4 0.3172 0.5719 0.000 0.000 0.160 0.840
#> SRR1440467 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR807995 2 0.5280 0.7140 0.000 0.752 0.124 0.124
#> SRR1476485 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.2921 0.8227 0.860 0.000 0.000 0.140
#> SRR1456051 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1473275 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1444083 4 0.3610 0.5699 0.200 0.000 0.000 0.800
#> SRR1313807 3 0.3356 0.7011 0.000 0.000 0.824 0.176
#> SRR1470751 1 0.6090 0.5230 0.564 0.000 0.052 0.384
#> SRR1403434 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0336 0.8880 0.992 0.000 0.000 0.008
#> SRR1093861 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1325290 4 0.2401 0.6162 0.092 0.000 0.004 0.904
#> SRR1070689 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.2973 0.7568 0.000 0.000 0.856 0.144
#> SRR1321877 3 0.0592 0.8654 0.000 0.000 0.984 0.016
#> SRR815711 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1433476 3 0.0336 0.8666 0.000 0.000 0.992 0.008
#> SRR1101883 4 0.4643 0.5593 0.000 0.000 0.344 0.656
#> SRR1433729 2 0.7414 0.2327 0.000 0.492 0.188 0.320
#> SRR1341877 4 0.2216 0.6269 0.000 0.000 0.092 0.908
#> SRR1090556 4 0.0000 0.6723 0.000 0.000 0.000 1.000
#> SRR1357389 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1404227 3 0.2921 0.7888 0.000 0.000 0.860 0.140
#> SRR1376830 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.2281 0.8438 0.904 0.000 0.000 0.096
#> SRR1080294 2 0.2589 0.8345 0.000 0.884 0.116 0.000
#> SRR1336314 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1102152 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1478637 4 0.3764 0.5076 0.000 0.000 0.216 0.784
#> SRR1443776 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1120939 4 0.4830 0.5299 0.000 0.000 0.392 0.608
#> SRR1080117 3 0.0000 0.8681 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0188 0.9251 0.000 0.996 0.004 0.000
#> SRR1091865 1 0.4164 0.7133 0.736 0.000 0.000 0.264
#> SRR1361072 1 0.0592 0.8863 0.984 0.000 0.000 0.016
#> SRR1487890 1 0.0000 0.8900 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.0921 0.8607 0.000 0.000 0.972 0.028
#> SRR1389384 1 0.6893 0.5221 0.564 0.000 0.136 0.300
#> SRR1316096 2 0.0000 0.9272 0.000 1.000 0.000 0.000
#> SRR1408512 1 0.5147 0.4481 0.536 0.000 0.004 0.460
#> SRR1447547 4 0.0000 0.6723 0.000 0.000 0.000 1.000
#> SRR1354053 2 0.0000 0.9272 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.6343 -0.0609 0.000 0.240 0.208 0.548 0.004
#> SRR1499040 1 0.6206 0.3615 0.504 0.000 0.152 0.344 0.000
#> SRR1322312 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1100991 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1349479 3 0.2230 0.7712 0.000 0.000 0.884 0.116 0.000
#> SRR1431248 5 0.4268 0.4760 0.000 0.000 0.000 0.444 0.556
#> SRR1405054 5 0.1478 0.6786 0.064 0.000 0.000 0.000 0.936
#> SRR1312266 1 0.3366 0.6747 0.768 0.000 0.000 0.232 0.000
#> SRR1409790 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1352507 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1383763 1 0.0566 0.8224 0.984 0.000 0.000 0.004 0.012
#> SRR1468314 2 0.5218 0.3969 0.000 0.624 0.068 0.308 0.000
#> SRR1473674 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1390499 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.4291 -0.0989 0.000 0.464 0.000 0.536 0.000
#> SRR1455653 4 0.4291 -0.0989 0.000 0.464 0.000 0.536 0.000
#> SRR1335236 2 0.2074 0.6745 0.000 0.896 0.104 0.000 0.000
#> SRR1095383 2 0.5600 0.3577 0.000 0.588 0.096 0.316 0.000
#> SRR1479489 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1073435 5 0.3631 0.6709 0.000 0.000 0.072 0.104 0.824
#> SRR659649 3 0.4304 0.0957 0.000 0.000 0.516 0.000 0.484
#> SRR1395999 1 0.3942 0.6324 0.728 0.000 0.000 0.260 0.012
#> SRR1105248 5 0.4182 0.1969 0.000 0.000 0.400 0.000 0.600
#> SRR1338257 1 0.4045 0.5520 0.644 0.000 0.000 0.356 0.000
#> SRR1499395 3 0.1544 0.8295 0.000 0.000 0.932 0.000 0.068
#> SRR1350002 2 0.0404 0.7657 0.000 0.988 0.000 0.012 0.000
#> SRR1489757 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1414637 5 0.4648 0.4380 0.012 0.000 0.000 0.464 0.524
#> SRR1478113 4 0.4037 0.0370 0.000 0.288 0.004 0.704 0.004
#> SRR1322477 4 0.6277 -0.1647 0.384 0.000 0.000 0.464 0.152
#> SRR1478789 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1414185 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1376852 1 0.4762 0.5973 0.700 0.000 0.000 0.236 0.064
#> SRR1323491 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1338103 5 0.3177 0.6515 0.000 0.000 0.000 0.208 0.792
#> SRR1472012 5 0.6819 -0.0201 0.312 0.000 0.000 0.340 0.348
#> SRR1340325 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 3 0.3730 0.5371 0.000 0.000 0.712 0.288 0.000
#> SRR1089446 5 0.3491 0.6216 0.000 0.000 0.228 0.004 0.768
#> SRR1344445 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1412969 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1071668 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1075804 1 0.6124 0.3351 0.520 0.000 0.000 0.336 0.144
#> SRR1383283 3 0.3715 0.5994 0.000 0.000 0.736 0.004 0.260
#> SRR1350239 5 0.0324 0.6912 0.000 0.000 0.004 0.004 0.992
#> SRR1353878 1 0.4045 0.5511 0.644 0.000 0.000 0.356 0.000
#> SRR1375721 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.6127 0.2965 0.484 0.000 0.000 0.384 0.132
#> SRR1090095 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.3607 0.0673 0.000 0.244 0.000 0.752 0.004
#> SRR1098737 1 0.6282 0.2914 0.496 0.000 0.000 0.340 0.164
#> SRR1349409 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 5 0.0324 0.6912 0.000 0.000 0.004 0.004 0.992
#> SRR1407179 5 0.2891 0.6643 0.000 0.000 0.000 0.176 0.824
#> SRR1095913 3 0.5915 0.0933 0.000 0.108 0.508 0.000 0.384
#> SRR1403544 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR807971 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1436228 5 0.3274 0.6495 0.000 0.000 0.000 0.220 0.780
#> SRR1445218 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1485438 2 0.5236 0.0793 0.000 0.492 0.000 0.464 0.044
#> SRR1358143 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.5176 0.4547 0.572 0.000 0.000 0.380 0.048
#> SRR1380806 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 4 0.6324 0.0505 0.000 0.032 0.196 0.616 0.156
#> SRR1346734 4 0.4430 -0.0879 0.000 0.456 0.000 0.540 0.004
#> SRR1414515 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 4 0.6462 -0.2017 0.404 0.000 0.004 0.436 0.156
#> SRR1349320 4 0.4430 -0.0879 0.000 0.456 0.000 0.540 0.004
#> SRR1317554 4 0.4291 -0.0989 0.000 0.464 0.000 0.536 0.000
#> SRR1076022 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1339573 3 0.0510 0.8725 0.000 0.000 0.984 0.000 0.016
#> SRR1455878 1 0.6706 0.1298 0.404 0.000 0.000 0.348 0.248
#> SRR1446203 5 0.3774 0.5414 0.000 0.000 0.296 0.000 0.704
#> SRR1387397 5 0.3983 0.5701 0.000 0.000 0.000 0.340 0.660
#> SRR1402590 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.3983 0.5667 0.000 0.000 0.000 0.340 0.660
#> SRR1331488 1 0.4323 0.5525 0.656 0.000 0.000 0.332 0.012
#> SRR1499675 5 0.5673 0.4848 0.000 0.000 0.252 0.132 0.616
#> SRR1440467 3 0.0162 0.8790 0.000 0.000 0.996 0.000 0.004
#> SRR807995 2 0.2020 0.6693 0.000 0.900 0.000 0.100 0.000
#> SRR1476485 4 0.4434 -0.0933 0.000 0.460 0.000 0.536 0.004
#> SRR1388214 1 0.4846 0.4783 0.588 0.000 0.000 0.384 0.028
#> SRR1456051 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1444083 5 0.4682 0.5127 0.024 0.000 0.000 0.356 0.620
#> SRR1313807 3 0.1043 0.8525 0.000 0.000 0.960 0.000 0.040
#> SRR1470751 4 0.6419 -0.1646 0.384 0.000 0.004 0.460 0.152
#> SRR1403434 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1390540 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1325290 5 0.5557 0.3478 0.068 0.000 0.000 0.464 0.468
#> SRR1070689 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.2362 0.8004 0.000 0.000 0.900 0.076 0.024
#> SRR1321877 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR815711 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1433476 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1101883 5 0.2648 0.6712 0.000 0.000 0.152 0.000 0.848
#> SRR1433729 3 0.7862 0.0292 0.000 0.136 0.384 0.124 0.356
#> SRR1341877 5 0.5114 0.5448 0.000 0.000 0.052 0.340 0.608
#> SRR1090556 5 0.3999 0.5646 0.000 0.000 0.000 0.344 0.656
#> SRR1357389 5 0.2690 0.6700 0.000 0.000 0.156 0.000 0.844
#> SRR1404227 3 0.0671 0.8697 0.000 0.000 0.980 0.004 0.016
#> SRR1376830 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.2270 0.7611 0.904 0.000 0.000 0.076 0.020
#> SRR1080294 2 0.6569 0.2840 0.000 0.464 0.232 0.304 0.000
#> SRR1336314 4 0.4430 -0.0879 0.000 0.456 0.000 0.540 0.004
#> SRR1102152 1 0.2280 0.7526 0.880 0.000 0.000 0.120 0.000
#> SRR1345244 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 5 0.5250 0.4760 0.000 0.000 0.048 0.416 0.536
#> SRR1443776 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 5 0.3586 0.5897 0.000 0.000 0.264 0.000 0.736
#> SRR1080117 3 0.0000 0.8810 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1091865 1 0.6132 0.2889 0.480 0.000 0.000 0.388 0.132
#> SRR1361072 1 0.0290 0.8285 0.992 0.000 0.000 0.008 0.000
#> SRR1487890 1 0.0000 0.8325 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.0162 0.8792 0.000 0.000 0.996 0.004 0.000
#> SRR1389384 4 0.6679 -0.1781 0.388 0.000 0.020 0.456 0.136
#> SRR1316096 2 0.0000 0.7748 0.000 1.000 0.000 0.000 0.000
#> SRR1408512 4 0.6439 -0.1247 0.356 0.000 0.000 0.460 0.184
#> SRR1447547 5 0.2970 0.6658 0.000 0.000 0.004 0.168 0.828
#> SRR1354053 2 0.4210 0.2765 0.000 0.588 0.000 0.412 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0260 0.95388 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1349562 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.0458 0.93341 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR1499040 5 0.3861 0.44429 0.352 0.000 0.000 0.000 0.640 0.008
#> SRR1322312 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1100991 3 0.0260 0.84443 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1349479 6 0.0146 0.92686 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1431248 5 0.1003 0.72145 0.000 0.000 0.020 0.016 0.964 0.000
#> SRR1405054 3 0.0405 0.84372 0.004 0.000 0.988 0.000 0.008 0.000
#> SRR1312266 5 0.3857 0.15233 0.468 0.000 0.000 0.000 0.532 0.000
#> SRR1409790 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1352507 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1383763 1 0.0976 0.93232 0.968 0.000 0.008 0.008 0.016 0.000
#> SRR1468314 2 0.3405 0.65958 0.000 0.724 0.000 0.272 0.000 0.004
#> SRR1473674 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1390499 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.2300 0.85262 0.000 0.144 0.000 0.856 0.000 0.000
#> SRR1455653 4 0.1663 0.90570 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1335236 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1095383 2 0.3555 0.64531 0.000 0.712 0.000 0.280 0.000 0.008
#> SRR1479489 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1073435 3 0.4354 0.70840 0.000 0.000 0.752 0.016 0.120 0.112
#> SRR659649 3 0.3823 0.17033 0.000 0.000 0.564 0.000 0.000 0.436
#> SRR1395999 5 0.3982 0.29088 0.460 0.000 0.004 0.000 0.536 0.000
#> SRR1105248 3 0.4676 0.14794 0.000 0.000 0.528 0.044 0.000 0.428
#> SRR1338257 5 0.3050 0.63102 0.236 0.000 0.000 0.000 0.764 0.000
#> SRR1499395 6 0.1957 0.82875 0.000 0.000 0.112 0.000 0.000 0.888
#> SRR1350002 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489757 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1414637 5 0.0891 0.72023 0.000 0.000 0.008 0.024 0.968 0.000
#> SRR1478113 4 0.0508 0.94461 0.000 0.012 0.000 0.984 0.000 0.004
#> SRR1322477 5 0.0260 0.72248 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1478789 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1414185 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 1 0.3650 0.52192 0.716 0.000 0.008 0.004 0.272 0.000
#> SRR1323491 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338103 3 0.3797 0.52962 0.000 0.000 0.692 0.016 0.292 0.000
#> SRR1472012 5 0.5484 0.49836 0.136 0.000 0.240 0.016 0.608 0.000
#> SRR1340325 1 0.0458 0.94771 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1087321 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.2941 0.61401 0.000 0.000 0.000 0.000 0.780 0.220
#> SRR1089446 3 0.2019 0.81211 0.000 0.000 0.900 0.000 0.012 0.088
#> SRR1344445 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1412969 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1071668 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1075804 5 0.4358 0.43601 0.380 0.000 0.008 0.016 0.596 0.000
#> SRR1383283 6 0.2662 0.79832 0.000 0.000 0.120 0.000 0.024 0.856
#> SRR1350239 3 0.2162 0.80165 0.000 0.000 0.896 0.088 0.012 0.004
#> SRR1353878 5 0.3592 0.46618 0.344 0.000 0.000 0.000 0.656 0.000
#> SRR1375721 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 5 0.1663 0.71912 0.088 0.000 0.000 0.000 0.912 0.000
#> SRR1090095 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.93389 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1098737 5 0.4641 0.49316 0.340 0.000 0.028 0.016 0.616 0.000
#> SRR1349409 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 3 0.2162 0.80117 0.000 0.000 0.896 0.088 0.012 0.004
#> SRR1407179 3 0.4039 0.39908 0.000 0.000 0.632 0.016 0.352 0.000
#> SRR1095913 6 0.3857 -0.00635 0.000 0.000 0.468 0.000 0.000 0.532
#> SRR1403544 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0146 0.95719 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR807971 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1436228 5 0.3558 0.54112 0.000 0.000 0.248 0.016 0.736 0.000
#> SRR1445218 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1485438 5 0.4002 0.51751 0.000 0.284 0.008 0.016 0.692 0.000
#> SRR1358143 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1328760 5 0.1863 0.70811 0.104 0.000 0.000 0.000 0.896 0.000
#> SRR1380806 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1379426 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1087007 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1086256 5 0.2258 0.70799 0.000 0.008 0.008 0.056 0.908 0.020
#> SRR1346734 4 0.0547 0.94865 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1414515 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.0547 0.72407 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1349320 4 0.0547 0.94865 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1317554 4 0.2300 0.85262 0.000 0.144 0.000 0.856 0.000 0.000
#> SRR1076022 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339573 6 0.0458 0.91917 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1455878 5 0.4403 0.53792 0.304 0.000 0.048 0.000 0.648 0.000
#> SRR1446203 3 0.2527 0.75509 0.000 0.000 0.832 0.000 0.000 0.168
#> SRR1387397 5 0.3515 0.45233 0.000 0.000 0.324 0.000 0.676 0.000
#> SRR1402590 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.3634 0.35736 0.000 0.000 0.356 0.000 0.644 0.000
#> SRR1331488 5 0.3804 0.36205 0.424 0.000 0.000 0.000 0.576 0.000
#> SRR1499675 3 0.6448 0.09744 0.000 0.000 0.368 0.016 0.344 0.272
#> SRR1440467 6 0.0146 0.92664 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR807995 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476485 4 0.0547 0.94865 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1388214 5 0.1814 0.70954 0.100 0.000 0.000 0.000 0.900 0.000
#> SRR1456051 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1473275 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1444083 5 0.4074 0.40380 0.016 0.000 0.324 0.004 0.656 0.000
#> SRR1313807 6 0.0260 0.92257 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1470751 5 0.0146 0.72314 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1403434 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1325290 5 0.0717 0.72118 0.000 0.000 0.008 0.016 0.976 0.000
#> SRR1070689 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.2036 0.84599 0.000 0.000 0.008 0.016 0.064 0.912
#> SRR1321877 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1433476 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1101883 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1433729 6 0.5652 0.09637 0.000 0.020 0.392 0.092 0.000 0.496
#> SRR1341877 5 0.5046 0.37181 0.000 0.000 0.320 0.016 0.604 0.060
#> SRR1090556 5 0.4110 0.32659 0.000 0.000 0.376 0.016 0.608 0.000
#> SRR1357389 3 0.0260 0.84851 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1404227 6 0.0508 0.92033 0.000 0.000 0.012 0.004 0.000 0.984
#> SRR1376830 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.1728 0.87526 0.924 0.000 0.008 0.004 0.064 0.000
#> SRR1080294 2 0.3756 0.65107 0.000 0.712 0.000 0.268 0.000 0.020
#> SRR1336314 4 0.0547 0.94865 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1102152 1 0.3747 0.25043 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1345244 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1478637 5 0.1630 0.71845 0.000 0.000 0.024 0.016 0.940 0.020
#> SRR1443776 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 3 0.2260 0.78107 0.000 0.000 0.860 0.000 0.000 0.140
#> SRR1080117 6 0.0000 0.92860 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091865 5 0.1327 0.71753 0.064 0.000 0.000 0.000 0.936 0.000
#> SRR1361072 1 0.2416 0.77149 0.844 0.000 0.000 0.000 0.156 0.000
#> SRR1487890 1 0.0000 0.96032 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.0146 0.92663 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1389384 5 0.0146 0.72261 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1316096 2 0.0000 0.90490 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1408512 5 0.0000 0.72226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1447547 3 0.4166 0.70220 0.000 0.000 0.748 0.088 0.160 0.004
#> SRR1354053 2 0.3428 0.61235 0.000 0.696 0.000 0.304 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.539 0.929 0.935 0.338515 0.688 0.688
#> 3 3 0.944 0.927 0.971 0.934817 0.561 0.404
#> 4 4 0.748 0.781 0.902 0.000941 0.732 0.443
#> 5 5 0.650 0.449 0.768 0.089925 0.838 0.612
#> 6 6 0.723 0.705 0.775 0.108847 0.843 0.543
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.915 1.000 0.000
#> SRR1349562 1 0.0000 0.915 1.000 0.000
#> SRR1353376 2 0.0376 0.999 0.004 0.996
#> SRR1499040 1 0.1414 0.919 0.980 0.020
#> SRR1322312 1 0.0000 0.915 1.000 0.000
#> SRR1324412 1 0.6343 0.909 0.840 0.160
#> SRR1100991 1 0.6343 0.909 0.840 0.160
#> SRR1349479 1 0.6247 0.909 0.844 0.156
#> SRR1431248 1 0.3114 0.919 0.944 0.056
#> SRR1405054 1 0.6343 0.909 0.840 0.160
#> SRR1312266 1 0.1633 0.919 0.976 0.024
#> SRR1409790 1 0.6343 0.909 0.840 0.160
#> SRR1352507 1 0.6343 0.909 0.840 0.160
#> SRR1383763 1 0.0000 0.915 1.000 0.000
#> SRR1468314 2 0.0376 0.999 0.004 0.996
#> SRR1473674 2 0.0376 0.999 0.004 0.996
#> SRR1390499 1 0.0000 0.915 1.000 0.000
#> SRR821043 2 0.0376 0.999 0.004 0.996
#> SRR1455653 2 0.0376 0.999 0.004 0.996
#> SRR1335236 2 0.0376 0.999 0.004 0.996
#> SRR1095383 2 0.0376 0.999 0.004 0.996
#> SRR1479489 1 0.1184 0.918 0.984 0.016
#> SRR1310433 2 0.0376 0.999 0.004 0.996
#> SRR1073435 1 0.6247 0.909 0.844 0.156
#> SRR659649 1 0.6343 0.909 0.840 0.160
#> SRR1395999 1 0.0000 0.915 1.000 0.000
#> SRR1105248 1 0.6247 0.909 0.844 0.156
#> SRR1338257 1 0.0000 0.915 1.000 0.000
#> SRR1499395 1 0.6343 0.909 0.840 0.160
#> SRR1350002 2 0.0376 0.999 0.004 0.996
#> SRR1489757 1 0.6343 0.909 0.840 0.160
#> SRR1414637 1 0.2778 0.920 0.952 0.048
#> SRR1478113 2 0.0376 0.999 0.004 0.996
#> SRR1322477 1 0.1633 0.919 0.976 0.024
#> SRR1478789 1 0.6247 0.909 0.844 0.156
#> SRR1414185 1 0.6343 0.909 0.840 0.160
#> SRR1069141 2 0.0376 0.999 0.004 0.996
#> SRR1376852 1 0.0000 0.915 1.000 0.000
#> SRR1323491 1 0.0000 0.915 1.000 0.000
#> SRR1338103 1 0.3114 0.919 0.944 0.056
#> SRR1472012 1 0.3114 0.919 0.944 0.056
#> SRR1340325 1 0.1633 0.919 0.976 0.024
#> SRR1087321 1 0.6343 0.909 0.840 0.160
#> SRR1488790 1 0.0000 0.915 1.000 0.000
#> SRR1334866 1 0.6247 0.909 0.844 0.156
#> SRR1089446 1 0.6343 0.909 0.840 0.160
#> SRR1344445 1 0.6343 0.909 0.840 0.160
#> SRR1412969 1 0.6247 0.909 0.844 0.156
#> SRR1071668 1 0.6343 0.909 0.840 0.160
#> SRR1075804 1 0.0000 0.915 1.000 0.000
#> SRR1383283 1 0.6247 0.909 0.844 0.156
#> SRR1350239 1 0.6247 0.909 0.844 0.156
#> SRR1353878 1 0.0000 0.915 1.000 0.000
#> SRR1375721 1 0.0000 0.915 1.000 0.000
#> SRR1083983 1 0.1414 0.919 0.980 0.020
#> SRR1090095 1 0.0000 0.915 1.000 0.000
#> SRR1414792 1 0.0000 0.915 1.000 0.000
#> SRR1075102 2 0.0376 0.999 0.004 0.996
#> SRR1098737 1 0.0000 0.915 1.000 0.000
#> SRR1349409 1 0.0000 0.915 1.000 0.000
#> SRR1413008 1 0.6247 0.909 0.844 0.156
#> SRR1407179 1 0.6247 0.909 0.844 0.156
#> SRR1095913 1 0.6247 0.909 0.844 0.156
#> SRR1403544 1 0.0000 0.915 1.000 0.000
#> SRR1490546 1 0.0000 0.915 1.000 0.000
#> SRR807971 1 0.6343 0.909 0.840 0.160
#> SRR1436228 1 0.5629 0.913 0.868 0.132
#> SRR1445218 2 0.0376 0.999 0.004 0.996
#> SRR1485438 2 0.1633 0.977 0.024 0.976
#> SRR1358143 1 0.0000 0.915 1.000 0.000
#> SRR1328760 1 0.0000 0.915 1.000 0.000
#> SRR1380806 1 0.0938 0.917 0.988 0.012
#> SRR1379426 1 0.6343 0.909 0.840 0.160
#> SRR1087007 1 0.6343 0.909 0.840 0.160
#> SRR1086256 1 0.6343 0.907 0.840 0.160
#> SRR1346734 2 0.0376 0.999 0.004 0.996
#> SRR1414515 1 0.0000 0.915 1.000 0.000
#> SRR1082151 1 0.1633 0.919 0.976 0.024
#> SRR1349320 2 0.0376 0.999 0.004 0.996
#> SRR1317554 2 0.0376 0.999 0.004 0.996
#> SRR1076022 2 0.0376 0.999 0.004 0.996
#> SRR1339573 1 0.6343 0.909 0.840 0.160
#> SRR1455878 1 0.0376 0.915 0.996 0.004
#> SRR1446203 1 0.6343 0.909 0.840 0.160
#> SRR1387397 1 0.1843 0.919 0.972 0.028
#> SRR1402590 1 0.0000 0.915 1.000 0.000
#> SRR1317532 1 0.1414 0.919 0.980 0.020
#> SRR1331488 1 0.0000 0.915 1.000 0.000
#> SRR1499675 1 0.6247 0.909 0.844 0.156
#> SRR1440467 1 0.6343 0.909 0.840 0.160
#> SRR807995 2 0.0376 0.999 0.004 0.996
#> SRR1476485 2 0.0376 0.999 0.004 0.996
#> SRR1388214 1 0.0000 0.915 1.000 0.000
#> SRR1456051 1 0.0000 0.915 1.000 0.000
#> SRR1473275 1 0.6343 0.909 0.840 0.160
#> SRR1444083 1 0.0000 0.915 1.000 0.000
#> SRR1313807 1 0.6247 0.909 0.844 0.156
#> SRR1470751 1 0.1633 0.919 0.976 0.024
#> SRR1403434 1 0.6343 0.909 0.840 0.160
#> SRR1390540 1 0.0000 0.915 1.000 0.000
#> SRR1093861 2 0.0376 0.999 0.004 0.996
#> SRR1325290 1 0.1633 0.919 0.976 0.024
#> SRR1070689 1 0.0000 0.915 1.000 0.000
#> SRR1384049 1 0.0000 0.915 1.000 0.000
#> SRR1081184 1 0.0000 0.915 1.000 0.000
#> SRR1324295 1 0.0000 0.915 1.000 0.000
#> SRR1365313 1 0.6247 0.909 0.844 0.156
#> SRR1321877 1 0.6343 0.909 0.840 0.160
#> SRR815711 1 0.6343 0.909 0.840 0.160
#> SRR1433476 1 0.6247 0.909 0.844 0.156
#> SRR1101883 1 0.6343 0.909 0.840 0.160
#> SRR1433729 1 0.6343 0.907 0.840 0.160
#> SRR1341877 1 0.4161 0.918 0.916 0.084
#> SRR1090556 1 0.3114 0.919 0.944 0.056
#> SRR1357389 1 0.6343 0.909 0.840 0.160
#> SRR1404227 1 0.6247 0.909 0.844 0.156
#> SRR1376830 1 0.0000 0.915 1.000 0.000
#> SRR1500661 1 0.0000 0.915 1.000 0.000
#> SRR1080294 2 0.0376 0.999 0.004 0.996
#> SRR1336314 2 0.0376 0.999 0.004 0.996
#> SRR1102152 1 0.0000 0.915 1.000 0.000
#> SRR1345244 1 0.6343 0.909 0.840 0.160
#> SRR1478637 1 0.6247 0.909 0.844 0.156
#> SRR1443776 1 0.6343 0.909 0.840 0.160
#> SRR1120939 1 0.6343 0.909 0.840 0.160
#> SRR1080117 1 0.6343 0.909 0.840 0.160
#> SRR1102899 2 0.0376 0.999 0.004 0.996
#> SRR1091865 1 0.1184 0.918 0.984 0.016
#> SRR1361072 1 0.0000 0.915 1.000 0.000
#> SRR1487890 1 0.0000 0.915 1.000 0.000
#> SRR1349456 1 0.6247 0.909 0.844 0.156
#> SRR1389384 1 0.1633 0.919 0.976 0.024
#> SRR1316096 2 0.0376 0.999 0.004 0.996
#> SRR1408512 1 0.0938 0.917 0.988 0.012
#> SRR1447547 1 0.6247 0.909 0.844 0.156
#> SRR1354053 2 0.0376 0.999 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1499040 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1322312 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1349479 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1431248 1 0.5968 0.4677 0.636 0.364 0.000
#> SRR1405054 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1312266 2 0.6305 -0.0335 0.484 0.516 0.000
#> SRR1409790 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1335236 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1095383 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1073435 2 0.1163 0.9545 0.000 0.972 0.028
#> SRR659649 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1105248 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1338257 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1414637 2 0.3482 0.8328 0.128 0.872 0.000
#> SRR1478113 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1478789 3 0.5678 0.5396 0.000 0.316 0.684
#> SRR1414185 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1338103 1 0.2959 0.8680 0.900 0.100 0.000
#> SRR1472012 1 0.2711 0.8802 0.912 0.088 0.000
#> SRR1340325 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1334866 1 0.6314 0.3957 0.604 0.392 0.004
#> SRR1089446 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1412969 3 0.1964 0.9202 0.000 0.056 0.944
#> SRR1071668 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1383283 2 0.0237 0.9767 0.000 0.996 0.004
#> SRR1350239 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1353878 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1413008 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1407179 3 0.0237 0.9714 0.000 0.004 0.996
#> SRR1095913 2 0.0237 0.9767 0.000 0.996 0.004
#> SRR1403544 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1436228 1 0.6079 0.4120 0.612 0.388 0.000
#> SRR1445218 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1082151 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1349320 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1331488 1 0.5363 0.6314 0.724 0.276 0.000
#> SRR1499675 1 0.6053 0.6430 0.720 0.260 0.020
#> SRR1440467 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1313807 2 0.2165 0.9156 0.000 0.936 0.064
#> SRR1470751 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1403434 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1365313 2 0.0747 0.9661 0.000 0.984 0.016
#> SRR1321877 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1433476 2 0.0237 0.9766 0.000 0.996 0.004
#> SRR1101883 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1341877 1 0.3686 0.8245 0.860 0.140 0.000
#> SRR1090556 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1478637 1 0.6140 0.3716 0.596 0.404 0.000
#> SRR1443776 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.9749 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1349456 3 0.6215 0.2514 0.000 0.428 0.572
#> SRR1389384 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1316096 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.9555 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.9797 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.9797 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0188 0.833 0.000 0.004 0.000 0.996
#> SRR1499040 3 0.4992 0.278 0.476 0.000 0.524 0.000
#> SRR1322312 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0592 0.808 0.016 0.000 0.984 0.000
#> SRR1100991 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1349479 4 0.6147 0.169 0.000 0.048 0.464 0.488
#> SRR1431248 3 0.5396 0.296 0.464 0.000 0.524 0.012
#> SRR1405054 3 0.0921 0.803 0.028 0.000 0.972 0.000
#> SRR1312266 1 0.1637 0.898 0.940 0.000 0.000 0.060
#> SRR1409790 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.3400 0.727 0.820 0.000 0.180 0.000
#> SRR1468314 4 0.4855 0.370 0.000 0.400 0.000 0.600
#> SRR1473674 2 0.1637 0.977 0.000 0.940 0.000 0.060
#> SRR1390499 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.0469 0.933 0.000 0.988 0.000 0.012
#> SRR1095383 4 0.4916 0.366 0.000 0.424 0.000 0.576
#> SRR1479489 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.1389 0.978 0.000 0.952 0.000 0.048
#> SRR1073435 3 0.1854 0.799 0.000 0.048 0.940 0.012
#> SRR659649 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1105248 3 0.2197 0.795 0.000 0.048 0.928 0.024
#> SRR1338257 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1499395 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.1716 0.974 0.000 0.936 0.000 0.064
#> SRR1489757 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1414637 3 0.6292 0.352 0.416 0.000 0.524 0.060
#> SRR1478113 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1322477 3 0.4992 0.278 0.476 0.000 0.524 0.000
#> SRR1478789 3 0.1722 0.801 0.000 0.048 0.944 0.008
#> SRR1414185 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.1389 0.978 0.000 0.952 0.000 0.048
#> SRR1376852 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1338103 1 0.5250 0.312 0.640 0.004 0.344 0.012
#> SRR1472012 3 0.5925 0.332 0.444 0.028 0.524 0.004
#> SRR1340325 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1334866 3 0.4026 0.753 0.092 0.048 0.848 0.012
#> SRR1089446 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.1576 0.802 0.000 0.048 0.948 0.004
#> SRR1071668 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1383283 3 0.1854 0.799 0.000 0.048 0.940 0.012
#> SRR1350239 3 0.2197 0.795 0.000 0.048 0.928 0.024
#> SRR1353878 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1375721 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1090095 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1413008 3 0.2197 0.795 0.000 0.048 0.928 0.024
#> SRR1407179 3 0.1489 0.803 0.000 0.044 0.952 0.004
#> SRR1095913 3 0.1854 0.799 0.000 0.048 0.940 0.012
#> SRR1403544 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR807971 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1436228 3 0.6257 0.523 0.332 0.048 0.608 0.012
#> SRR1445218 2 0.1557 0.975 0.000 0.944 0.000 0.056
#> SRR1485438 3 0.6235 0.235 0.000 0.420 0.524 0.056
#> SRR1358143 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1087007 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1086256 3 0.6484 0.326 0.016 0.388 0.552 0.044
#> SRR1346734 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1082151 3 0.6292 0.352 0.416 0.000 0.524 0.060
#> SRR1349320 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.2868 0.734 0.000 0.136 0.000 0.864
#> SRR1076022 2 0.1557 0.978 0.000 0.944 0.000 0.056
#> SRR1339573 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.4888 0.112 0.588 0.000 0.412 0.000
#> SRR1402590 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1331488 3 0.5325 0.287 0.468 0.004 0.524 0.004
#> SRR1499675 3 0.5438 0.666 0.208 0.048 0.732 0.012
#> SRR1440467 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR807995 2 0.1637 0.977 0.000 0.940 0.000 0.060
#> SRR1476485 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.3725 0.724 0.812 0.008 0.180 0.000
#> SRR1456051 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1313807 3 0.1722 0.801 0.000 0.048 0.944 0.008
#> SRR1470751 3 0.6292 0.352 0.416 0.000 0.524 0.060
#> SRR1403434 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1093861 2 0.1389 0.974 0.000 0.952 0.000 0.048
#> SRR1325290 3 0.4992 0.278 0.476 0.000 0.524 0.000
#> SRR1070689 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.1854 0.799 0.000 0.048 0.940 0.012
#> SRR1321877 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR815711 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1433476 3 0.2586 0.786 0.000 0.048 0.912 0.040
#> SRR1101883 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1433729 3 0.4589 0.643 0.000 0.048 0.784 0.168
#> SRR1341877 3 0.6487 0.398 0.404 0.048 0.536 0.012
#> SRR1090556 3 0.4992 0.278 0.476 0.000 0.524 0.000
#> SRR1357389 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.0817 0.809 0.000 0.024 0.976 0.000
#> SRR1376830 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.6398 0.433 0.000 0.344 0.080 0.576
#> SRR1336314 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1345244 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1478637 3 0.6405 0.456 0.372 0.048 0.568 0.012
#> SRR1443776 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.813 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0469 0.933 0.000 0.988 0.000 0.012
#> SRR1091865 1 0.2589 0.827 0.884 0.000 0.116 0.000
#> SRR1361072 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> SRR1487890 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.1854 0.799 0.000 0.048 0.940 0.012
#> SRR1389384 3 0.4992 0.278 0.476 0.000 0.524 0.000
#> SRR1316096 2 0.1474 0.978 0.000 0.948 0.000 0.052
#> SRR1408512 1 0.3123 0.768 0.844 0.000 0.156 0.000
#> SRR1447547 3 0.6693 0.454 0.368 0.048 0.560 0.024
#> SRR1354053 4 0.0000 0.835 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.3876 0.5765 0.684 0.000 0.000 0.000 0.316
#> SRR1349562 1 0.0404 0.5064 0.988 0.000 0.000 0.000 0.012
#> SRR1353376 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1499040 1 0.6372 -0.4389 0.492 0.000 0.324 0.000 0.184
#> SRR1322312 1 0.0162 0.5167 0.996 0.000 0.000 0.000 0.004
#> SRR1324412 3 0.5603 0.5778 0.076 0.012 0.624 0.000 0.288
#> SRR1100991 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1349479 4 0.5562 0.3863 0.000 0.000 0.296 0.604 0.100
#> SRR1431248 1 0.7466 -0.6744 0.404 0.004 0.296 0.028 0.268
#> SRR1405054 3 0.6593 -0.0597 0.212 0.000 0.420 0.000 0.368
#> SRR1312266 1 0.5024 0.5567 0.572 0.004 0.000 0.028 0.396
#> SRR1409790 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1352507 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1383763 1 0.4793 0.2140 0.700 0.000 0.232 0.000 0.068
#> SRR1468314 4 0.3381 0.7583 0.000 0.176 0.000 0.808 0.016
#> SRR1473674 2 0.1043 0.9140 0.000 0.960 0.000 0.040 0.000
#> SRR1390499 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1455653 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1335236 2 0.1121 0.9131 0.000 0.956 0.000 0.044 0.000
#> SRR1095383 4 0.3163 0.7705 0.000 0.164 0.000 0.824 0.012
#> SRR1479489 1 0.5470 0.5014 0.612 0.000 0.092 0.000 0.296
#> SRR1310433 2 0.0510 0.9099 0.000 0.984 0.000 0.016 0.000
#> SRR1073435 3 0.5655 0.4406 0.000 0.004 0.600 0.304 0.092
#> SRR659649 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.4150 0.5800 0.612 0.000 0.000 0.000 0.388
#> SRR1105248 3 0.5895 0.1320 0.000 0.000 0.456 0.444 0.100
#> SRR1338257 1 0.4249 0.5632 0.568 0.000 0.000 0.000 0.432
#> SRR1499395 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 2 0.1121 0.9121 0.000 0.956 0.000 0.044 0.000
#> SRR1489757 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1414637 1 0.7617 -0.6845 0.404 0.008 0.292 0.032 0.264
#> SRR1478113 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1322477 1 0.6673 -0.5356 0.440 0.000 0.276 0.000 0.284
#> SRR1478789 3 0.1026 0.6436 0.000 0.004 0.968 0.024 0.004
#> SRR1414185 3 0.3612 0.6889 0.000 0.000 0.732 0.000 0.268
#> SRR1069141 2 0.0963 0.9111 0.000 0.964 0.000 0.036 0.000
#> SRR1376852 1 0.4808 0.5686 0.620 0.000 0.032 0.000 0.348
#> SRR1323491 1 0.4219 0.5718 0.584 0.000 0.000 0.000 0.416
#> SRR1338103 1 0.6926 -0.6205 0.428 0.000 0.300 0.008 0.264
#> SRR1472012 1 0.6930 -0.5269 0.464 0.004 0.292 0.008 0.232
#> SRR1340325 1 0.4150 0.5800 0.612 0.000 0.000 0.000 0.388
#> SRR1087321 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 3 0.7436 -0.8080 0.304 0.004 0.412 0.028 0.252
#> SRR1089446 3 0.3814 0.6875 0.000 0.004 0.720 0.000 0.276
#> SRR1344445 3 0.3707 0.6865 0.000 0.000 0.716 0.000 0.284
#> SRR1412969 3 0.3967 0.6876 0.000 0.000 0.724 0.012 0.264
#> SRR1071668 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1075804 1 0.4150 0.5800 0.612 0.000 0.000 0.000 0.388
#> SRR1383283 3 0.5747 0.4109 0.000 0.004 0.576 0.328 0.092
#> SRR1350239 3 0.5895 0.1320 0.000 0.000 0.456 0.444 0.100
#> SRR1353878 1 0.4210 0.5735 0.588 0.000 0.000 0.000 0.412
#> SRR1375721 1 0.0162 0.5167 0.996 0.000 0.000 0.000 0.004
#> SRR1083983 1 0.5516 0.4936 0.608 0.000 0.096 0.000 0.296
#> SRR1090095 1 0.3876 0.5806 0.684 0.000 0.000 0.000 0.316
#> SRR1414792 1 0.0290 0.5183 0.992 0.000 0.000 0.000 0.008
#> SRR1075102 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1098737 1 0.4150 0.5800 0.612 0.000 0.000 0.000 0.388
#> SRR1349409 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 3 0.5895 0.1320 0.000 0.000 0.456 0.444 0.100
#> SRR1407179 3 0.0775 0.6521 0.004 0.004 0.980 0.004 0.008
#> SRR1095913 3 0.1630 0.6299 0.000 0.016 0.944 0.036 0.004
#> SRR1403544 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.4227 0.5698 0.580 0.000 0.000 0.000 0.420
#> SRR807971 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1436228 1 0.7483 -0.7336 0.388 0.004 0.320 0.028 0.260
#> SRR1445218 2 0.0510 0.9099 0.000 0.984 0.000 0.016 0.000
#> SRR1485438 2 0.7527 -0.1714 0.008 0.444 0.292 0.036 0.220
#> SRR1358143 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.4161 0.5794 0.608 0.000 0.000 0.000 0.392
#> SRR1380806 1 0.2127 0.5454 0.892 0.000 0.000 0.000 0.108
#> SRR1379426 3 0.3586 0.6895 0.000 0.000 0.736 0.000 0.264
#> SRR1087007 3 0.3508 0.6906 0.000 0.000 0.748 0.000 0.252
#> SRR1086256 5 0.8337 0.0000 0.296 0.032 0.292 0.048 0.332
#> SRR1346734 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1414515 1 0.0162 0.5167 0.996 0.000 0.000 0.000 0.004
#> SRR1082151 1 0.7468 -0.6663 0.404 0.004 0.292 0.028 0.272
#> SRR1349320 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1317554 4 0.1792 0.8289 0.000 0.084 0.000 0.916 0.000
#> SRR1076022 2 0.0794 0.9160 0.000 0.972 0.000 0.028 0.000
#> SRR1339573 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 1 0.5099 0.5481 0.612 0.000 0.052 0.000 0.336
#> SRR1446203 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1387397 1 0.5717 0.0543 0.608 0.000 0.260 0.000 0.132
#> SRR1402590 1 0.0404 0.5064 0.988 0.000 0.000 0.000 0.012
#> SRR1317532 1 0.4299 0.5784 0.608 0.000 0.004 0.000 0.388
#> SRR1331488 1 0.4524 0.5656 0.572 0.000 0.004 0.004 0.420
#> SRR1499675 3 0.7058 -0.7395 0.388 0.004 0.424 0.024 0.160
#> SRR1440467 3 0.3424 0.6908 0.000 0.000 0.760 0.000 0.240
#> SRR807995 2 0.1043 0.9140 0.000 0.960 0.000 0.040 0.000
#> SRR1476485 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1388214 1 0.4249 0.5632 0.568 0.000 0.000 0.000 0.432
#> SRR1456051 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1444083 1 0.4249 0.5632 0.568 0.000 0.000 0.000 0.432
#> SRR1313807 3 0.5607 0.4425 0.000 0.004 0.604 0.304 0.088
#> SRR1470751 1 0.7487 -0.6814 0.396 0.004 0.292 0.028 0.280
#> SRR1403434 3 0.3586 0.6895 0.000 0.000 0.736 0.000 0.264
#> SRR1390540 1 0.4210 0.5735 0.588 0.000 0.000 0.000 0.412
#> SRR1093861 2 0.0703 0.9145 0.000 0.976 0.000 0.024 0.000
#> SRR1325290 1 0.6683 -0.5813 0.436 0.000 0.292 0.000 0.272
#> SRR1070689 1 0.0404 0.5064 0.988 0.000 0.000 0.000 0.012
#> SRR1384049 1 0.3774 0.5765 0.704 0.000 0.000 0.000 0.296
#> SRR1081184 1 0.0404 0.5064 0.988 0.000 0.000 0.000 0.012
#> SRR1324295 1 0.0404 0.5064 0.988 0.000 0.000 0.000 0.012
#> SRR1365313 3 0.6626 -0.4588 0.280 0.004 0.560 0.028 0.128
#> SRR1321877 3 0.0162 0.6596 0.000 0.000 0.996 0.000 0.004
#> SRR815711 3 0.3707 0.6865 0.000 0.000 0.716 0.000 0.284
#> SRR1433476 3 0.5708 0.3824 0.000 0.000 0.556 0.348 0.096
#> SRR1101883 3 0.3707 0.6865 0.000 0.000 0.716 0.000 0.284
#> SRR1433729 4 0.5630 0.6971 0.000 0.140 0.056 0.708 0.096
#> SRR1341877 1 0.7230 -0.6812 0.412 0.000 0.320 0.024 0.244
#> SRR1090556 1 0.6671 -0.5715 0.440 0.000 0.292 0.000 0.268
#> SRR1357389 3 0.4086 0.6829 0.000 0.012 0.704 0.000 0.284
#> SRR1404227 3 0.0486 0.6559 0.000 0.004 0.988 0.004 0.004
#> SRR1376830 1 0.1478 0.5343 0.936 0.000 0.000 0.000 0.064
#> SRR1500661 1 0.4150 0.5800 0.612 0.000 0.000 0.000 0.388
#> SRR1080294 4 0.3463 0.7735 0.000 0.156 0.008 0.820 0.016
#> SRR1336314 4 0.0162 0.8567 0.000 0.004 0.000 0.996 0.000
#> SRR1102152 1 0.4201 0.5750 0.592 0.000 0.000 0.000 0.408
#> SRR1345244 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 3 0.7488 -0.8373 0.316 0.004 0.388 0.028 0.264
#> SRR1443776 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 3 0.0000 0.6616 0.000 0.000 1.000 0.000 0.000
#> SRR1080117 3 0.3586 0.6895 0.000 0.000 0.736 0.000 0.264
#> SRR1102899 2 0.1121 0.9131 0.000 0.956 0.000 0.044 0.000
#> SRR1091865 1 0.5353 0.5053 0.576 0.000 0.064 0.000 0.360
#> SRR1361072 1 0.4192 0.5764 0.596 0.000 0.000 0.000 0.404
#> SRR1487890 1 0.0000 0.5149 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.1653 0.6284 0.000 0.024 0.944 0.028 0.004
#> SRR1389384 1 0.6683 -0.5813 0.436 0.000 0.292 0.000 0.272
#> SRR1316096 2 0.0404 0.9080 0.000 0.988 0.000 0.012 0.000
#> SRR1408512 1 0.5717 0.4336 0.572 0.000 0.104 0.000 0.324
#> SRR1447547 4 0.6242 -0.1503 0.004 0.000 0.428 0.444 0.124
#> SRR1354053 4 0.1671 0.8324 0.000 0.076 0.000 0.924 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.5508 0.7341 0.564 0.000 0.224 0.000 0.212 0.000
#> SRR1349562 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1499040 5 0.4350 0.4442 0.292 0.000 0.000 0.000 0.660 0.048
#> SRR1322312 1 0.0865 0.6494 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1324412 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1100991 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1349479 4 0.2669 0.7842 0.000 0.000 0.008 0.836 0.000 0.156
#> SRR1431248 5 0.4308 0.7367 0.048 0.000 0.016 0.088 0.792 0.056
#> SRR1405054 3 0.6884 0.4792 0.088 0.000 0.468 0.000 0.192 0.252
#> SRR1312266 1 0.5834 0.7158 0.520 0.000 0.244 0.004 0.232 0.000
#> SRR1409790 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1352507 3 0.3244 0.9057 0.000 0.000 0.732 0.000 0.000 0.268
#> SRR1383763 5 0.3756 -0.0155 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1468314 4 0.2212 0.8273 0.000 0.112 0.008 0.880 0.000 0.000
#> SRR1473674 2 0.1444 0.9260 0.000 0.928 0.000 0.072 0.000 0.000
#> SRR1390499 1 0.0260 0.6419 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR821043 4 0.0363 0.8579 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1455653 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1095383 4 0.2266 0.8291 0.000 0.108 0.012 0.880 0.000 0.000
#> SRR1479489 1 0.5667 0.7194 0.532 0.000 0.240 0.000 0.228 0.000
#> SRR1310433 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1073435 6 0.5216 0.4062 0.000 0.000 0.004 0.256 0.128 0.612
#> SRR659649 6 0.1444 0.7588 0.000 0.000 0.072 0.000 0.000 0.928
#> SRR1395999 1 0.5587 0.7303 0.548 0.000 0.240 0.000 0.212 0.000
#> SRR1105248 4 0.3445 0.6491 0.000 0.000 0.008 0.732 0.000 0.260
#> SRR1338257 1 0.5684 0.7272 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR1499395 6 0.1814 0.7174 0.000 0.000 0.100 0.000 0.000 0.900
#> SRR1350002 2 0.1663 0.9128 0.000 0.912 0.000 0.088 0.000 0.000
#> SRR1489757 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1414637 5 0.3847 0.7437 0.064 0.000 0.000 0.068 0.812 0.056
#> SRR1478113 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1322477 5 0.2455 0.7172 0.080 0.000 0.016 0.000 0.888 0.016
#> SRR1478789 6 0.0291 0.8052 0.000 0.000 0.004 0.000 0.004 0.992
#> SRR1414185 6 0.0632 0.8050 0.000 0.000 0.024 0.000 0.000 0.976
#> SRR1069141 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 1 0.5571 0.7267 0.552 0.000 0.220 0.000 0.228 0.000
#> SRR1323491 1 0.5684 0.7272 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR1338103 5 0.2837 0.7532 0.088 0.000 0.000 0.000 0.856 0.056
#> SRR1472012 5 0.4445 0.4662 0.288 0.000 0.000 0.000 0.656 0.056
#> SRR1340325 1 0.5565 0.7309 0.552 0.000 0.240 0.000 0.208 0.000
#> SRR1087321 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 1 0.0146 0.6412 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1334866 5 0.3812 0.5613 0.016 0.000 0.004 0.000 0.712 0.268
#> SRR1089446 6 0.5469 -0.3960 0.000 0.000 0.408 0.000 0.124 0.468
#> SRR1344445 3 0.3563 0.8428 0.000 0.000 0.664 0.000 0.000 0.336
#> SRR1412969 6 0.0458 0.8079 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1071668 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1075804 1 0.5583 0.7304 0.548 0.000 0.244 0.000 0.208 0.000
#> SRR1383283 6 0.3942 0.3715 0.000 0.000 0.004 0.368 0.004 0.624
#> SRR1350239 4 0.4408 0.5456 0.000 0.000 0.056 0.664 0.000 0.280
#> SRR1353878 1 0.5646 0.7293 0.536 0.000 0.244 0.000 0.220 0.000
#> SRR1375721 1 0.0865 0.6494 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1083983 1 0.5911 0.5634 0.432 0.000 0.212 0.000 0.356 0.000
#> SRR1090095 1 0.5088 0.7242 0.628 0.000 0.220 0.000 0.152 0.000
#> SRR1414792 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1098737 1 0.5684 0.7263 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR1349409 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.4515 0.4905 0.000 0.000 0.056 0.640 0.000 0.304
#> SRR1407179 6 0.2300 0.6745 0.000 0.000 0.000 0.000 0.144 0.856
#> SRR1095913 6 0.1615 0.7553 0.000 0.000 0.004 0.064 0.004 0.928
#> SRR1403544 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.5684 0.7272 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR807971 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1436228 5 0.3731 0.6326 0.024 0.000 0.000 0.008 0.756 0.212
#> SRR1445218 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1485438 5 0.6581 0.2421 0.000 0.308 0.000 0.168 0.468 0.056
#> SRR1358143 1 0.0260 0.6425 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1328760 1 0.5684 0.7193 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR1380806 1 0.3501 0.6911 0.804 0.000 0.116 0.000 0.080 0.000
#> SRR1379426 6 0.0547 0.8072 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1087007 6 0.0547 0.8072 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1086256 5 0.4819 0.5894 0.016 0.008 0.000 0.180 0.712 0.084
#> SRR1346734 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.0865 0.6494 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1082151 5 0.4152 0.7170 0.024 0.000 0.016 0.116 0.792 0.052
#> SRR1349320 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1317554 4 0.1958 0.8339 0.000 0.100 0.004 0.896 0.000 0.000
#> SRR1076022 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339573 6 0.1910 0.7112 0.000 0.000 0.108 0.000 0.000 0.892
#> SRR1455878 1 0.5565 0.7309 0.552 0.000 0.240 0.000 0.208 0.000
#> SRR1446203 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1387397 1 0.6206 0.6009 0.448 0.000 0.224 0.000 0.316 0.012
#> SRR1402590 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.5855 0.6886 0.484 0.000 0.240 0.000 0.276 0.000
#> SRR1331488 1 0.6023 0.5490 0.392 0.000 0.244 0.000 0.364 0.000
#> SRR1499675 5 0.4371 0.2709 0.020 0.000 0.004 0.000 0.580 0.396
#> SRR1440467 6 0.0547 0.8072 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR807995 2 0.1327 0.9314 0.000 0.936 0.000 0.064 0.000 0.000
#> SRR1476485 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.6004 0.6347 0.416 0.000 0.244 0.000 0.340 0.000
#> SRR1456051 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1473275 6 0.3424 0.6379 0.000 0.000 0.092 0.000 0.096 0.812
#> SRR1444083 1 0.5961 0.6687 0.444 0.000 0.244 0.000 0.312 0.000
#> SRR1313807 6 0.3329 0.5611 0.000 0.000 0.004 0.236 0.004 0.756
#> SRR1470751 5 0.4195 0.7131 0.024 0.000 0.016 0.120 0.788 0.052
#> SRR1403434 6 0.0547 0.8072 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1390540 1 0.5684 0.7272 0.528 0.000 0.244 0.000 0.228 0.000
#> SRR1093861 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1325290 5 0.2608 0.7529 0.080 0.000 0.000 0.000 0.872 0.048
#> SRR1070689 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.5488 0.7317 0.568 0.000 0.220 0.000 0.212 0.000
#> SRR1081184 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.4284 0.1736 0.012 0.000 0.004 0.000 0.440 0.544
#> SRR1321877 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 3 0.3684 0.7799 0.000 0.000 0.628 0.000 0.000 0.372
#> SRR1433476 6 0.4098 -0.0677 0.000 0.000 0.008 0.496 0.000 0.496
#> SRR1101883 3 0.3578 0.8378 0.000 0.000 0.660 0.000 0.000 0.340
#> SRR1433729 4 0.3000 0.8250 0.000 0.088 0.012 0.856 0.000 0.044
#> SRR1341877 5 0.3073 0.7532 0.080 0.000 0.000 0.000 0.840 0.080
#> SRR1090556 5 0.2660 0.7516 0.084 0.000 0.000 0.000 0.868 0.048
#> SRR1357389 3 0.3198 0.9116 0.000 0.000 0.740 0.000 0.000 0.260
#> SRR1404227 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1376830 1 0.2266 0.6692 0.880 0.000 0.012 0.000 0.108 0.000
#> SRR1500661 1 0.5565 0.7309 0.552 0.000 0.240 0.000 0.208 0.000
#> SRR1080294 4 0.2266 0.8291 0.000 0.108 0.012 0.880 0.000 0.000
#> SRR1336314 4 0.0000 0.8609 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1102152 1 0.5738 0.7120 0.516 0.000 0.244 0.000 0.240 0.000
#> SRR1345244 6 0.0146 0.8088 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1478637 5 0.4353 0.6605 0.020 0.000 0.004 0.060 0.752 0.164
#> SRR1443776 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 6 0.0000 0.8089 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1080117 6 0.0547 0.8072 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1102899 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091865 5 0.3118 0.6358 0.092 0.000 0.072 0.000 0.836 0.000
#> SRR1361072 1 0.5703 0.7255 0.524 0.000 0.244 0.000 0.232 0.000
#> SRR1487890 1 0.0000 0.6397 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.1493 0.7634 0.000 0.000 0.004 0.056 0.004 0.936
#> SRR1389384 5 0.2925 0.7532 0.080 0.000 0.008 0.000 0.860 0.052
#> SRR1316096 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1408512 5 0.5853 -0.3015 0.312 0.000 0.216 0.000 0.472 0.000
#> SRR1447547 4 0.4972 0.3252 0.000 0.000 0.008 0.564 0.056 0.372
#> SRR1354053 4 0.1814 0.8342 0.000 0.100 0.000 0.900 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.969 0.947 0.978 0.4942 0.503 0.503
#> 3 3 0.915 0.894 0.957 0.3319 0.738 0.526
#> 4 4 0.944 0.884 0.954 0.0647 0.927 0.794
#> 5 5 0.797 0.773 0.888 0.0751 0.900 0.689
#> 6 6 0.781 0.743 0.862 0.0524 0.938 0.764
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.987 1.000 0.000
#> SRR1349562 1 0.0000 0.987 1.000 0.000
#> SRR1353376 2 0.0000 0.964 0.000 1.000
#> SRR1499040 1 0.0000 0.987 1.000 0.000
#> SRR1322312 1 0.0000 0.987 1.000 0.000
#> SRR1324412 1 0.0000 0.987 1.000 0.000
#> SRR1100991 1 0.0000 0.987 1.000 0.000
#> SRR1349479 2 0.0000 0.964 0.000 1.000
#> SRR1431248 1 0.8661 0.583 0.712 0.288
#> SRR1405054 1 0.0000 0.987 1.000 0.000
#> SRR1312266 1 0.0000 0.987 1.000 0.000
#> SRR1409790 1 0.0000 0.987 1.000 0.000
#> SRR1352507 1 0.0000 0.987 1.000 0.000
#> SRR1383763 1 0.0000 0.987 1.000 0.000
#> SRR1468314 2 0.0000 0.964 0.000 1.000
#> SRR1473674 2 0.0000 0.964 0.000 1.000
#> SRR1390499 1 0.0000 0.987 1.000 0.000
#> SRR821043 2 0.0000 0.964 0.000 1.000
#> SRR1455653 2 0.0000 0.964 0.000 1.000
#> SRR1335236 2 0.0000 0.964 0.000 1.000
#> SRR1095383 2 0.0000 0.964 0.000 1.000
#> SRR1479489 1 0.0000 0.987 1.000 0.000
#> SRR1310433 2 0.0000 0.964 0.000 1.000
#> SRR1073435 2 0.0000 0.964 0.000 1.000
#> SRR659649 2 0.9460 0.466 0.364 0.636
#> SRR1395999 1 0.0000 0.987 1.000 0.000
#> SRR1105248 2 0.0000 0.964 0.000 1.000
#> SRR1338257 1 0.0000 0.987 1.000 0.000
#> SRR1499395 1 0.0000 0.987 1.000 0.000
#> SRR1350002 2 0.0000 0.964 0.000 1.000
#> SRR1489757 1 0.0000 0.987 1.000 0.000
#> SRR1414637 1 0.9393 0.436 0.644 0.356
#> SRR1478113 2 0.0000 0.964 0.000 1.000
#> SRR1322477 1 0.0000 0.987 1.000 0.000
#> SRR1478789 2 0.0000 0.964 0.000 1.000
#> SRR1414185 2 0.9087 0.552 0.324 0.676
#> SRR1069141 2 0.0000 0.964 0.000 1.000
#> SRR1376852 1 0.0000 0.987 1.000 0.000
#> SRR1323491 1 0.0000 0.987 1.000 0.000
#> SRR1338103 1 0.0000 0.987 1.000 0.000
#> SRR1472012 1 0.0000 0.987 1.000 0.000
#> SRR1340325 1 0.0000 0.987 1.000 0.000
#> SRR1087321 2 0.0000 0.964 0.000 1.000
#> SRR1488790 1 0.0000 0.987 1.000 0.000
#> SRR1334866 2 0.4431 0.881 0.092 0.908
#> SRR1089446 1 0.0000 0.987 1.000 0.000
#> SRR1344445 1 0.0000 0.987 1.000 0.000
#> SRR1412969 2 0.0000 0.964 0.000 1.000
#> SRR1071668 1 0.0000 0.987 1.000 0.000
#> SRR1075804 1 0.0000 0.987 1.000 0.000
#> SRR1383283 2 0.0000 0.964 0.000 1.000
#> SRR1350239 2 0.7950 0.698 0.240 0.760
#> SRR1353878 1 0.0000 0.987 1.000 0.000
#> SRR1375721 1 0.0000 0.987 1.000 0.000
#> SRR1083983 1 0.0000 0.987 1.000 0.000
#> SRR1090095 1 0.0000 0.987 1.000 0.000
#> SRR1414792 1 0.0000 0.987 1.000 0.000
#> SRR1075102 2 0.0000 0.964 0.000 1.000
#> SRR1098737 1 0.0000 0.987 1.000 0.000
#> SRR1349409 1 0.0000 0.987 1.000 0.000
#> SRR1413008 2 0.8081 0.685 0.248 0.752
#> SRR1407179 1 0.0000 0.987 1.000 0.000
#> SRR1095913 2 0.0000 0.964 0.000 1.000
#> SRR1403544 1 0.0000 0.987 1.000 0.000
#> SRR1490546 1 0.0000 0.987 1.000 0.000
#> SRR807971 1 0.0000 0.987 1.000 0.000
#> SRR1436228 2 0.9815 0.322 0.420 0.580
#> SRR1445218 2 0.0000 0.964 0.000 1.000
#> SRR1485438 2 0.0000 0.964 0.000 1.000
#> SRR1358143 1 0.0000 0.987 1.000 0.000
#> SRR1328760 1 0.0000 0.987 1.000 0.000
#> SRR1380806 1 0.0000 0.987 1.000 0.000
#> SRR1379426 2 0.1843 0.942 0.028 0.972
#> SRR1087007 2 0.0000 0.964 0.000 1.000
#> SRR1086256 2 0.0000 0.964 0.000 1.000
#> SRR1346734 2 0.0000 0.964 0.000 1.000
#> SRR1414515 1 0.0000 0.987 1.000 0.000
#> SRR1082151 2 0.9248 0.519 0.340 0.660
#> SRR1349320 2 0.0000 0.964 0.000 1.000
#> SRR1317554 2 0.0000 0.964 0.000 1.000
#> SRR1076022 2 0.0000 0.964 0.000 1.000
#> SRR1339573 1 0.0000 0.987 1.000 0.000
#> SRR1455878 1 0.0000 0.987 1.000 0.000
#> SRR1446203 2 0.0000 0.964 0.000 1.000
#> SRR1387397 1 0.0000 0.987 1.000 0.000
#> SRR1402590 1 0.0000 0.987 1.000 0.000
#> SRR1317532 1 0.0000 0.987 1.000 0.000
#> SRR1331488 1 0.0000 0.987 1.000 0.000
#> SRR1499675 1 0.0000 0.987 1.000 0.000
#> SRR1440467 2 0.0000 0.964 0.000 1.000
#> SRR807995 2 0.0000 0.964 0.000 1.000
#> SRR1476485 2 0.0000 0.964 0.000 1.000
#> SRR1388214 1 0.0000 0.987 1.000 0.000
#> SRR1456051 1 0.0000 0.987 1.000 0.000
#> SRR1473275 1 0.0000 0.987 1.000 0.000
#> SRR1444083 1 0.0000 0.987 1.000 0.000
#> SRR1313807 2 0.0000 0.964 0.000 1.000
#> SRR1470751 1 0.8555 0.592 0.720 0.280
#> SRR1403434 2 0.0376 0.961 0.004 0.996
#> SRR1390540 1 0.0000 0.987 1.000 0.000
#> SRR1093861 2 0.0000 0.964 0.000 1.000
#> SRR1325290 1 0.0000 0.987 1.000 0.000
#> SRR1070689 1 0.0000 0.987 1.000 0.000
#> SRR1384049 1 0.0000 0.987 1.000 0.000
#> SRR1081184 1 0.0000 0.987 1.000 0.000
#> SRR1324295 1 0.0000 0.987 1.000 0.000
#> SRR1365313 2 0.0672 0.958 0.008 0.992
#> SRR1321877 2 0.0376 0.961 0.004 0.996
#> SRR815711 1 0.0000 0.987 1.000 0.000
#> SRR1433476 2 0.0000 0.964 0.000 1.000
#> SRR1101883 1 0.0000 0.987 1.000 0.000
#> SRR1433729 2 0.0000 0.964 0.000 1.000
#> SRR1341877 1 0.0000 0.987 1.000 0.000
#> SRR1090556 1 0.0000 0.987 1.000 0.000
#> SRR1357389 1 0.0000 0.987 1.000 0.000
#> SRR1404227 2 0.0000 0.964 0.000 1.000
#> SRR1376830 1 0.0000 0.987 1.000 0.000
#> SRR1500661 1 0.0000 0.987 1.000 0.000
#> SRR1080294 2 0.0000 0.964 0.000 1.000
#> SRR1336314 2 0.0000 0.964 0.000 1.000
#> SRR1102152 1 0.0000 0.987 1.000 0.000
#> SRR1345244 2 0.0672 0.958 0.008 0.992
#> SRR1478637 2 0.0000 0.964 0.000 1.000
#> SRR1443776 2 0.0000 0.964 0.000 1.000
#> SRR1120939 2 0.0000 0.964 0.000 1.000
#> SRR1080117 2 0.0000 0.964 0.000 1.000
#> SRR1102899 2 0.0000 0.964 0.000 1.000
#> SRR1091865 1 0.0000 0.987 1.000 0.000
#> SRR1361072 1 0.0000 0.987 1.000 0.000
#> SRR1487890 1 0.0000 0.987 1.000 0.000
#> SRR1349456 2 0.0000 0.964 0.000 1.000
#> SRR1389384 1 0.0000 0.987 1.000 0.000
#> SRR1316096 2 0.0000 0.964 0.000 1.000
#> SRR1408512 1 0.0000 0.987 1.000 0.000
#> SRR1447547 2 0.0000 0.964 0.000 1.000
#> SRR1354053 2 0.0000 0.964 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1499040 1 0.4346 0.771 0.816 0.000 0.184
#> SRR1322312 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1324412 3 0.0892 0.955 0.020 0.000 0.980
#> SRR1100991 3 0.0747 0.959 0.016 0.000 0.984
#> SRR1349479 3 0.0747 0.960 0.000 0.016 0.984
#> SRR1431248 2 0.5882 0.459 0.348 0.652 0.000
#> SRR1405054 3 0.2448 0.887 0.076 0.000 0.924
#> SRR1312266 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1409790 3 0.0424 0.967 0.008 0.000 0.992
#> SRR1352507 3 0.0237 0.971 0.004 0.000 0.996
#> SRR1383763 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.980 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1335236 2 0.0747 0.884 0.000 0.984 0.016
#> SRR1095383 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1479489 1 0.1163 0.954 0.972 0.000 0.028
#> SRR1310433 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1073435 2 0.6215 0.303 0.000 0.572 0.428
#> SRR659649 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1105248 2 0.6045 0.420 0.000 0.620 0.380
#> SRR1338257 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1489757 3 0.0237 0.971 0.004 0.000 0.996
#> SRR1414637 2 0.6267 0.171 0.452 0.548 0.000
#> SRR1478113 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1322477 1 0.0424 0.973 0.992 0.008 0.000
#> SRR1478789 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1334866 2 0.6090 0.617 0.020 0.716 0.264
#> SRR1089446 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1383283 2 0.5859 0.496 0.000 0.656 0.344
#> SRR1350239 3 0.0661 0.966 0.004 0.008 0.988
#> SRR1353878 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1413008 3 0.0661 0.966 0.004 0.008 0.988
#> SRR1407179 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1095913 2 0.6305 0.113 0.000 0.516 0.484
#> SRR1403544 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.980 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1436228 2 0.8543 0.538 0.268 0.592 0.140
#> SRR1445218 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1082151 2 0.5016 0.664 0.240 0.760 0.000
#> SRR1349320 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1499675 1 0.6148 0.428 0.640 0.004 0.356
#> SRR1440467 3 0.0000 0.974 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1313807 3 0.6126 0.248 0.000 0.400 0.600
#> SRR1470751 1 0.6111 0.295 0.604 0.396 0.000
#> SRR1403434 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1365313 3 0.5621 0.498 0.000 0.308 0.692
#> SRR1321877 3 0.0000 0.974 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1433476 2 0.6308 0.105 0.000 0.508 0.492
#> SRR1101883 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1433729 2 0.0747 0.884 0.000 0.984 0.016
#> SRR1341877 1 0.1643 0.936 0.956 0.000 0.044
#> SRR1090556 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1080294 2 0.0747 0.884 0.000 0.984 0.016
#> SRR1336314 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1478637 2 0.0237 0.891 0.000 0.996 0.004
#> SRR1443776 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1102899 2 0.0237 0.891 0.000 0.996 0.004
#> SRR1091865 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.974 0.000 0.000 1.000
#> SRR1389384 1 0.0747 0.965 0.984 0.016 0.000
#> SRR1316096 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.980 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.893 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.893 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1499040 1 0.0469 0.978 0.988 0.012 0.000 0.000
#> SRR1322312 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1349479 3 0.2530 0.854 0.000 0.000 0.888 0.112
#> SRR1431248 1 0.0707 0.971 0.980 0.020 0.000 0.000
#> SRR1405054 3 0.0188 0.971 0.004 0.000 0.996 0.000
#> SRR1312266 1 0.0707 0.969 0.980 0.000 0.000 0.020
#> SRR1409790 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1468314 2 0.0469 0.839 0.000 0.988 0.000 0.012
#> SRR1473674 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1390499 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0188 0.857 0.000 0.004 0.000 0.996
#> SRR1455653 4 0.4522 0.467 0.000 0.320 0.000 0.680
#> SRR1335236 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1095383 2 0.3873 0.647 0.000 0.772 0.000 0.228
#> SRR1479489 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.0469 0.839 0.000 0.988 0.000 0.012
#> SRR1073435 2 0.5016 0.371 0.000 0.600 0.396 0.004
#> SRR659649 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.1557 0.829 0.000 0.000 0.056 0.944
#> SRR1338257 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1489757 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1414637 2 0.4713 0.366 0.360 0.640 0.000 0.000
#> SRR1478113 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1322477 1 0.0336 0.981 0.992 0.008 0.000 0.000
#> SRR1478789 3 0.2081 0.893 0.000 0.084 0.916 0.000
#> SRR1414185 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1376852 1 0.0188 0.983 0.996 0.004 0.000 0.000
#> SRR1323491 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.0469 0.978 0.988 0.012 0.000 0.000
#> SRR1472012 1 0.0469 0.978 0.988 0.012 0.000 0.000
#> SRR1340325 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.0000 0.841 0.000 1.000 0.000 0.000
#> SRR1089446 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1071668 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1383283 2 0.4482 0.593 0.000 0.728 0.264 0.008
#> SRR1350239 4 0.1474 0.832 0.000 0.000 0.052 0.948
#> SRR1353878 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.0188 0.983 0.996 0.004 0.000 0.000
#> SRR1090095 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.1118 0.842 0.000 0.000 0.036 0.964
#> SRR1407179 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1095913 2 0.5050 0.310 0.000 0.588 0.408 0.004
#> SRR1403544 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.0000 0.841 0.000 1.000 0.000 0.000
#> SRR1445218 2 0.0336 0.840 0.000 0.992 0.000 0.008
#> SRR1485438 2 0.0000 0.841 0.000 1.000 0.000 0.000
#> SRR1358143 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1087007 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1086256 2 0.0000 0.841 0.000 1.000 0.000 0.000
#> SRR1346734 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.4933 0.250 0.432 0.568 0.000 0.000
#> SRR1349320 4 0.0188 0.858 0.000 0.004 0.000 0.996
#> SRR1317554 4 0.4713 0.369 0.000 0.360 0.000 0.640
#> SRR1076022 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1339573 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0188 0.983 0.996 0.004 0.000 0.000
#> SRR1446203 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1331488 4 0.4948 0.207 0.440 0.000 0.000 0.560
#> SRR1499675 1 0.4898 0.278 0.584 0.000 0.416 0.000
#> SRR1440467 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR807995 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1476485 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1456051 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1313807 3 0.2469 0.860 0.000 0.108 0.892 0.000
#> SRR1470751 1 0.2281 0.886 0.904 0.096 0.000 0.000
#> SRR1403434 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1325290 1 0.0336 0.981 0.992 0.008 0.000 0.000
#> SRR1070689 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.4605 0.502 0.000 0.664 0.336 0.000
#> SRR1321877 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR815711 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1433476 4 0.4804 0.352 0.000 0.000 0.384 0.616
#> SRR1101883 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1433729 2 0.4453 0.616 0.000 0.744 0.012 0.244
#> SRR1341877 1 0.0817 0.961 0.976 0.000 0.024 0.000
#> SRR1090556 1 0.0188 0.983 0.996 0.004 0.000 0.000
#> SRR1357389 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1376830 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1080294 2 0.2868 0.748 0.000 0.864 0.000 0.136
#> SRR1336314 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1478637 2 0.0000 0.841 0.000 1.000 0.000 0.000
#> SRR1443776 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0469 0.839 0.000 0.988 0.000 0.012
#> SRR1091865 1 0.0336 0.981 0.992 0.008 0.000 0.000
#> SRR1361072 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.4948 0.165 0.000 0.440 0.560 0.000
#> SRR1389384 1 0.1637 0.929 0.940 0.060 0.000 0.000
#> SRR1316096 2 0.0188 0.842 0.000 0.996 0.000 0.004
#> SRR1408512 1 0.0000 0.986 1.000 0.000 0.000 0.000
#> SRR1447547 4 0.0707 0.849 0.000 0.020 0.000 0.980
#> SRR1354053 2 0.3266 0.716 0.000 0.832 0.000 0.168
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1349562 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1353376 4 0.2471 0.7906 0.000 0.136 0.000 0.864 0.000
#> SRR1499040 5 0.6220 0.3256 0.188 0.000 0.272 0.000 0.540
#> SRR1322312 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0290 0.9710 0.000 0.000 0.992 0.000 0.008
#> SRR1100991 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1349479 3 0.2270 0.8951 0.000 0.020 0.904 0.076 0.000
#> SRR1431248 5 0.5086 0.4881 0.200 0.096 0.000 0.004 0.700
#> SRR1405054 3 0.0324 0.9706 0.004 0.000 0.992 0.000 0.004
#> SRR1312266 1 0.5043 0.6539 0.704 0.000 0.000 0.160 0.136
#> SRR1409790 3 0.0162 0.9731 0.000 0.000 0.996 0.000 0.004
#> SRR1352507 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1383763 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1468314 2 0.3395 0.5592 0.000 0.764 0.000 0.000 0.236
#> SRR1473674 5 0.3336 0.5385 0.000 0.228 0.000 0.000 0.772
#> SRR1390499 1 0.0404 0.9316 0.988 0.000 0.000 0.000 0.012
#> SRR821043 4 0.2300 0.8284 0.000 0.072 0.000 0.904 0.024
#> SRR1455653 4 0.5594 0.4400 0.000 0.136 0.000 0.632 0.232
#> SRR1335236 2 0.4294 0.0991 0.000 0.532 0.000 0.000 0.468
#> SRR1095383 2 0.3262 0.6204 0.000 0.840 0.000 0.036 0.124
#> SRR1479489 1 0.0798 0.9254 0.976 0.000 0.008 0.000 0.016
#> SRR1310433 2 0.3305 0.5628 0.000 0.776 0.000 0.000 0.224
#> SRR1073435 2 0.2157 0.6246 0.004 0.920 0.040 0.000 0.036
#> SRR659649 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.2116 0.8942 0.912 0.008 0.000 0.004 0.076
#> SRR1105248 4 0.0955 0.8516 0.000 0.004 0.028 0.968 0.000
#> SRR1338257 1 0.3111 0.8288 0.840 0.000 0.004 0.012 0.144
#> SRR1499395 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 5 0.2773 0.5960 0.000 0.164 0.000 0.000 0.836
#> SRR1489757 3 0.0162 0.9731 0.000 0.000 0.996 0.000 0.004
#> SRR1414637 5 0.4478 0.5898 0.144 0.100 0.000 0.000 0.756
#> SRR1478113 4 0.1732 0.8360 0.000 0.000 0.000 0.920 0.080
#> SRR1322477 1 0.2648 0.8320 0.848 0.000 0.000 0.000 0.152
#> SRR1478789 3 0.1831 0.9162 0.000 0.076 0.920 0.000 0.004
#> SRR1414185 3 0.0162 0.9733 0.000 0.004 0.996 0.000 0.000
#> SRR1069141 5 0.4242 0.1588 0.000 0.428 0.000 0.000 0.572
#> SRR1376852 1 0.1331 0.9132 0.952 0.008 0.000 0.000 0.040
#> SRR1323491 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1338103 1 0.4609 0.7213 0.744 0.152 0.000 0.000 0.104
#> SRR1472012 1 0.4262 0.7636 0.776 0.124 0.000 0.000 0.100
#> SRR1340325 1 0.2623 0.8454 0.884 0.000 0.096 0.004 0.016
#> SRR1087321 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 2 0.3864 0.5691 0.028 0.820 0.028 0.000 0.124
#> SRR1089446 3 0.1792 0.9056 0.000 0.084 0.916 0.000 0.000
#> SRR1344445 3 0.0162 0.9731 0.000 0.000 0.996 0.000 0.004
#> SRR1412969 3 0.0324 0.9726 0.000 0.004 0.992 0.000 0.004
#> SRR1071668 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1075804 1 0.1195 0.9160 0.960 0.012 0.000 0.000 0.028
#> SRR1383283 2 0.1179 0.6346 0.004 0.964 0.016 0.000 0.016
#> SRR1350239 4 0.3177 0.6929 0.000 0.000 0.208 0.792 0.000
#> SRR1353878 1 0.1282 0.9162 0.952 0.000 0.000 0.004 0.044
#> SRR1375721 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.2420 0.8832 0.896 0.008 0.008 0.000 0.088
#> SRR1090095 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1075102 4 0.0404 0.8535 0.000 0.000 0.000 0.988 0.012
#> SRR1098737 1 0.1059 0.9216 0.968 0.008 0.000 0.004 0.020
#> SRR1349409 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.2929 0.7264 0.000 0.000 0.180 0.820 0.000
#> SRR1407179 2 0.6083 0.0665 0.052 0.464 0.452 0.000 0.032
#> SRR1095913 3 0.4904 0.5448 0.000 0.240 0.688 0.000 0.072
#> SRR1403544 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1490546 1 0.0324 0.9316 0.992 0.000 0.000 0.004 0.004
#> SRR807971 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1436228 2 0.4657 0.4589 0.108 0.740 0.000 0.000 0.152
#> SRR1445218 2 0.3480 0.5454 0.000 0.752 0.000 0.000 0.248
#> SRR1485438 5 0.3661 0.4746 0.000 0.276 0.000 0.000 0.724
#> SRR1358143 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.1026 0.9229 0.968 0.000 0.004 0.004 0.024
#> SRR1380806 1 0.0324 0.9309 0.992 0.000 0.004 0.000 0.004
#> SRR1379426 3 0.0324 0.9726 0.000 0.004 0.992 0.000 0.004
#> SRR1087007 3 0.0794 0.9615 0.000 0.028 0.972 0.000 0.000
#> SRR1086256 2 0.2852 0.5762 0.000 0.828 0.000 0.000 0.172
#> SRR1346734 4 0.0162 0.8534 0.000 0.004 0.000 0.996 0.000
#> SRR1414515 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.1205 0.6374 0.004 0.040 0.000 0.000 0.956
#> SRR1349320 4 0.1478 0.8439 0.000 0.000 0.000 0.936 0.064
#> SRR1317554 2 0.4712 0.5651 0.000 0.732 0.000 0.168 0.100
#> SRR1076022 2 0.3366 0.5588 0.000 0.768 0.000 0.000 0.232
#> SRR1339573 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 1 0.2046 0.8970 0.916 0.000 0.016 0.000 0.068
#> SRR1446203 3 0.0162 0.9732 0.000 0.004 0.996 0.000 0.000
#> SRR1387397 1 0.2915 0.8336 0.860 0.116 0.000 0.000 0.024
#> SRR1402590 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.0162 0.9312 0.996 0.000 0.000 0.000 0.004
#> SRR1331488 1 0.4192 0.3360 0.596 0.000 0.000 0.404 0.000
#> SRR1499675 2 0.6834 0.1170 0.376 0.472 0.108 0.000 0.044
#> SRR1440467 3 0.1197 0.9450 0.000 0.048 0.952 0.000 0.000
#> SRR807995 5 0.2471 0.6120 0.000 0.136 0.000 0.000 0.864
#> SRR1476485 4 0.0162 0.8534 0.000 0.004 0.000 0.996 0.000
#> SRR1388214 1 0.0955 0.9239 0.968 0.000 0.000 0.004 0.028
#> SRR1456051 1 0.0290 0.9316 0.992 0.000 0.000 0.000 0.008
#> SRR1473275 3 0.0290 0.9718 0.000 0.000 0.992 0.000 0.008
#> SRR1444083 1 0.4328 0.7715 0.788 0.000 0.032 0.036 0.144
#> SRR1313807 2 0.2646 0.5874 0.000 0.868 0.124 0.004 0.004
#> SRR1470751 5 0.1485 0.6441 0.032 0.020 0.000 0.000 0.948
#> SRR1403434 3 0.0880 0.9583 0.000 0.032 0.968 0.000 0.000
#> SRR1390540 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1093861 5 0.4242 0.1554 0.000 0.428 0.000 0.000 0.572
#> SRR1325290 1 0.3940 0.7381 0.756 0.024 0.000 0.000 0.220
#> SRR1070689 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1324295 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1365313 2 0.1662 0.6333 0.004 0.936 0.056 0.000 0.004
#> SRR1321877 3 0.0324 0.9726 0.000 0.004 0.992 0.000 0.004
#> SRR815711 3 0.0963 0.9525 0.000 0.036 0.964 0.000 0.000
#> SRR1433476 2 0.6060 0.1350 0.000 0.492 0.124 0.384 0.000
#> SRR1101883 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1433729 2 0.2116 0.6382 0.000 0.924 0.008 0.040 0.028
#> SRR1341877 1 0.3966 0.7517 0.784 0.176 0.004 0.000 0.036
#> SRR1090556 1 0.5436 0.7099 0.724 0.120 0.032 0.004 0.120
#> SRR1357389 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1404227 2 0.4622 0.1571 0.000 0.548 0.440 0.000 0.012
#> SRR1376830 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1500661 1 0.0000 0.9321 1.000 0.000 0.000 0.000 0.000
#> SRR1080294 2 0.2517 0.6309 0.000 0.884 0.004 0.008 0.104
#> SRR1336314 4 0.1697 0.8403 0.000 0.008 0.000 0.932 0.060
#> SRR1102152 1 0.1082 0.9204 0.964 0.000 0.008 0.000 0.028
#> SRR1345244 3 0.0324 0.9730 0.000 0.004 0.992 0.000 0.004
#> SRR1478637 5 0.2707 0.5752 0.008 0.132 0.000 0.000 0.860
#> SRR1443776 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 3 0.0404 0.9692 0.000 0.012 0.988 0.000 0.000
#> SRR1080117 3 0.0000 0.9742 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.2424 0.6190 0.000 0.868 0.000 0.000 0.132
#> SRR1091865 5 0.3521 0.5365 0.232 0.000 0.000 0.004 0.764
#> SRR1361072 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1487890 1 0.0162 0.9320 0.996 0.000 0.000 0.000 0.004
#> SRR1349456 2 0.1638 0.6326 0.000 0.932 0.064 0.000 0.004
#> SRR1389384 5 0.2690 0.6102 0.156 0.000 0.000 0.000 0.844
#> SRR1316096 2 0.4283 0.1404 0.000 0.544 0.000 0.000 0.456
#> SRR1408512 1 0.1403 0.9117 0.952 0.024 0.000 0.000 0.024
#> SRR1447547 4 0.4169 0.6942 0.000 0.000 0.028 0.732 0.240
#> SRR1354053 2 0.6229 0.1127 0.000 0.464 0.000 0.144 0.392
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.3934 0.4891 0.000 0.000 0.000 0.676 0.020 0.304
#> SRR1499040 2 0.5572 0.4197 0.024 0.612 0.228 0.000 0.136 0.000
#> SRR1322312 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324412 3 0.0508 0.9423 0.004 0.000 0.984 0.000 0.012 0.000
#> SRR1100991 3 0.0458 0.9443 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1349479 3 0.3220 0.8562 0.000 0.004 0.840 0.056 0.004 0.096
#> SRR1431248 5 0.4178 0.4291 0.004 0.316 0.000 0.016 0.660 0.004
#> SRR1405054 3 0.0820 0.9381 0.012 0.000 0.972 0.000 0.016 0.000
#> SRR1312266 1 0.5437 0.4434 0.592 0.088 0.000 0.296 0.024 0.000
#> SRR1409790 3 0.0363 0.9430 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1352507 3 0.0146 0.9446 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1383763 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1468314 6 0.1663 0.6657 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1473674 2 0.3189 0.5813 0.000 0.760 0.000 0.000 0.004 0.236
#> SRR1390499 1 0.0717 0.9129 0.976 0.008 0.000 0.000 0.016 0.000
#> SRR821043 6 0.4141 0.1094 0.000 0.012 0.000 0.432 0.000 0.556
#> SRR1455653 4 0.5232 0.3044 0.000 0.116 0.000 0.564 0.000 0.320
#> SRR1335236 6 0.3881 0.3333 0.000 0.396 0.000 0.000 0.004 0.600
#> SRR1095383 6 0.1649 0.6415 0.000 0.000 0.000 0.036 0.032 0.932
#> SRR1479489 1 0.1528 0.8860 0.936 0.000 0.048 0.000 0.016 0.000
#> SRR1310433 6 0.1663 0.6648 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1073435 5 0.3744 0.5960 0.000 0.000 0.044 0.000 0.756 0.200
#> SRR659649 3 0.0000 0.9448 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1395999 1 0.4251 0.6741 0.716 0.076 0.000 0.000 0.208 0.000
#> SRR1105248 4 0.1049 0.8195 0.000 0.000 0.032 0.960 0.000 0.008
#> SRR1338257 1 0.4352 0.7673 0.780 0.120 0.036 0.016 0.048 0.000
#> SRR1499395 3 0.1152 0.9420 0.000 0.004 0.952 0.000 0.044 0.000
#> SRR1350002 2 0.2178 0.7129 0.000 0.868 0.000 0.000 0.000 0.132
#> SRR1489757 3 0.0260 0.9440 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1414637 2 0.3352 0.6893 0.000 0.792 0.000 0.000 0.176 0.032
#> SRR1478113 4 0.1616 0.8171 0.000 0.048 0.000 0.932 0.020 0.000
#> SRR1322477 1 0.2618 0.8403 0.860 0.116 0.000 0.000 0.024 0.000
#> SRR1478789 3 0.4624 0.6847 0.000 0.008 0.692 0.000 0.220 0.080
#> SRR1414185 3 0.2792 0.9109 0.000 0.004 0.876 0.016 0.076 0.028
#> SRR1069141 6 0.3868 0.1043 0.000 0.492 0.000 0.000 0.000 0.508
#> SRR1376852 1 0.3003 0.7829 0.812 0.016 0.000 0.000 0.172 0.000
#> SRR1323491 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338103 5 0.2393 0.6743 0.064 0.040 0.000 0.000 0.892 0.004
#> SRR1472012 5 0.3041 0.6270 0.128 0.040 0.000 0.000 0.832 0.000
#> SRR1340325 1 0.3603 0.7689 0.804 0.012 0.136 0.000 0.048 0.000
#> SRR1087321 3 0.0777 0.9449 0.000 0.004 0.972 0.000 0.024 0.000
#> SRR1488790 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.4193 0.5953 0.036 0.044 0.004 0.000 0.776 0.140
#> SRR1089446 3 0.1760 0.9299 0.004 0.000 0.928 0.000 0.020 0.048
#> SRR1344445 3 0.0260 0.9445 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1412969 3 0.2213 0.9132 0.000 0.004 0.888 0.000 0.100 0.008
#> SRR1071668 3 0.0363 0.9430 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1075804 1 0.3023 0.7088 0.768 0.000 0.000 0.000 0.232 0.000
#> SRR1383283 6 0.4241 0.1837 0.000 0.004 0.020 0.000 0.348 0.628
#> SRR1350239 4 0.2632 0.7303 0.000 0.000 0.164 0.832 0.004 0.000
#> SRR1353878 1 0.1151 0.9038 0.956 0.032 0.000 0.000 0.012 0.000
#> SRR1375721 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.4805 0.4703 0.608 0.052 0.008 0.000 0.332 0.000
#> SRR1090095 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1414792 1 0.0146 0.9171 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1075102 4 0.1092 0.8243 0.000 0.020 0.000 0.960 0.020 0.000
#> SRR1098737 1 0.3128 0.7792 0.812 0.008 0.000 0.012 0.168 0.000
#> SRR1349409 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1413008 4 0.2662 0.7417 0.000 0.004 0.152 0.840 0.004 0.000
#> SRR1407179 5 0.3534 0.6237 0.000 0.000 0.124 0.000 0.800 0.076
#> SRR1095913 5 0.6675 0.2964 0.000 0.064 0.276 0.000 0.476 0.184
#> SRR1403544 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0260 0.9163 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR807971 3 0.0260 0.9454 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1436228 5 0.4775 0.6312 0.048 0.068 0.000 0.000 0.724 0.160
#> SRR1445218 6 0.1814 0.6609 0.000 0.100 0.000 0.000 0.000 0.900
#> SRR1485438 2 0.3081 0.6139 0.000 0.776 0.000 0.000 0.004 0.220
#> SRR1358143 1 0.0260 0.9164 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1328760 1 0.0820 0.9114 0.972 0.012 0.000 0.000 0.016 0.000
#> SRR1380806 1 0.0520 0.9127 0.984 0.000 0.008 0.000 0.008 0.000
#> SRR1379426 3 0.2806 0.8782 0.000 0.004 0.844 0.000 0.136 0.016
#> SRR1087007 3 0.2776 0.8896 0.000 0.004 0.860 0.000 0.104 0.032
#> SRR1086256 5 0.4548 0.4053 0.000 0.056 0.000 0.000 0.632 0.312
#> SRR1346734 4 0.0363 0.8209 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1414515 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.0865 0.7622 0.000 0.964 0.000 0.000 0.000 0.036
#> SRR1349320 4 0.1720 0.8176 0.000 0.040 0.000 0.928 0.032 0.000
#> SRR1317554 6 0.1814 0.6454 0.000 0.000 0.000 0.100 0.000 0.900
#> SRR1076022 6 0.5039 0.5045 0.000 0.176 0.000 0.000 0.184 0.640
#> SRR1339573 3 0.0692 0.9442 0.000 0.004 0.976 0.000 0.020 0.000
#> SRR1455878 1 0.2647 0.8501 0.868 0.044 0.000 0.000 0.088 0.000
#> SRR1446203 3 0.0937 0.9396 0.000 0.000 0.960 0.000 0.040 0.000
#> SRR1387397 1 0.3765 0.3211 0.596 0.000 0.000 0.000 0.404 0.000
#> SRR1402590 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.0363 0.9160 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1331488 1 0.2092 0.8321 0.876 0.000 0.000 0.124 0.000 0.000
#> SRR1499675 5 0.2420 0.6697 0.032 0.000 0.008 0.000 0.892 0.068
#> SRR1440467 3 0.2809 0.8621 0.000 0.004 0.848 0.000 0.020 0.128
#> SRR807995 2 0.1663 0.7429 0.000 0.912 0.000 0.000 0.000 0.088
#> SRR1476485 4 0.0260 0.8212 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1388214 1 0.1448 0.9009 0.948 0.016 0.012 0.000 0.024 0.000
#> SRR1456051 1 0.0622 0.9133 0.980 0.008 0.000 0.000 0.012 0.000
#> SRR1473275 3 0.0858 0.9446 0.000 0.004 0.968 0.000 0.028 0.000
#> SRR1444083 1 0.5545 0.6239 0.680 0.152 0.112 0.032 0.024 0.000
#> SRR1313807 6 0.4319 0.0476 0.000 0.000 0.024 0.000 0.400 0.576
#> SRR1470751 2 0.0551 0.7617 0.000 0.984 0.000 0.004 0.004 0.008
#> SRR1403434 3 0.2009 0.9319 0.000 0.004 0.916 0.000 0.040 0.040
#> SRR1390540 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1093861 6 0.3843 0.2174 0.000 0.452 0.000 0.000 0.000 0.548
#> SRR1325290 5 0.5440 0.2633 0.348 0.132 0.000 0.000 0.520 0.000
#> SRR1070689 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1384049 1 0.0146 0.9172 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1081184 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.4329 0.0921 0.000 0.008 0.012 0.000 0.404 0.576
#> SRR1321877 3 0.2006 0.9130 0.000 0.004 0.892 0.000 0.104 0.000
#> SRR815711 3 0.0909 0.9448 0.000 0.000 0.968 0.000 0.020 0.012
#> SRR1433476 6 0.4861 0.5216 0.000 0.004 0.024 0.156 0.100 0.716
#> SRR1101883 3 0.1141 0.9382 0.000 0.000 0.948 0.000 0.052 0.000
#> SRR1433729 5 0.4556 0.1905 0.000 0.000 0.020 0.008 0.516 0.456
#> SRR1341877 5 0.2726 0.6620 0.112 0.000 0.000 0.000 0.856 0.032
#> SRR1090556 5 0.3206 0.6411 0.104 0.068 0.000 0.000 0.828 0.000
#> SRR1357389 3 0.0000 0.9448 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1404227 5 0.2609 0.6507 0.000 0.000 0.036 0.000 0.868 0.096
#> SRR1376830 1 0.0146 0.9171 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1500661 1 0.0260 0.9165 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1080294 6 0.1845 0.6142 0.000 0.000 0.004 0.008 0.072 0.916
#> SRR1336314 4 0.2214 0.7821 0.000 0.096 0.000 0.888 0.000 0.016
#> SRR1102152 1 0.1341 0.8952 0.948 0.000 0.028 0.000 0.024 0.000
#> SRR1345244 3 0.1219 0.9419 0.000 0.004 0.948 0.000 0.048 0.000
#> SRR1478637 2 0.3659 0.4340 0.000 0.636 0.000 0.000 0.364 0.000
#> SRR1443776 3 0.0935 0.9437 0.000 0.004 0.964 0.000 0.032 0.000
#> SRR1120939 3 0.1524 0.9233 0.000 0.000 0.932 0.000 0.060 0.008
#> SRR1080117 3 0.1555 0.9345 0.000 0.004 0.932 0.000 0.060 0.004
#> SRR1102899 6 0.0914 0.6582 0.000 0.016 0.000 0.000 0.016 0.968
#> SRR1091865 2 0.1693 0.7432 0.020 0.932 0.000 0.004 0.044 0.000
#> SRR1361072 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9175 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 5 0.4484 0.1580 0.000 0.008 0.016 0.000 0.516 0.460
#> SRR1389384 2 0.2458 0.7342 0.068 0.892 0.000 0.000 0.016 0.024
#> SRR1316096 6 0.2631 0.6149 0.000 0.180 0.000 0.000 0.000 0.820
#> SRR1408512 1 0.1814 0.8638 0.900 0.000 0.000 0.000 0.100 0.000
#> SRR1447547 4 0.4328 0.6445 0.000 0.212 0.000 0.708 0.080 0.000
#> SRR1354053 6 0.5296 0.4403 0.000 0.236 0.000 0.168 0.000 0.596
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.809 0.969 0.973 0.2306 0.737 0.737
#> 3 3 0.795 0.917 0.948 0.3546 0.990 0.987
#> 4 4 0.424 0.652 0.739 0.5218 0.995 0.993
#> 5 5 0.527 0.583 0.796 0.1113 0.795 0.718
#> 6 6 0.526 0.563 0.776 0.0341 0.950 0.906
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.996 1.000 0.000
#> SRR1349562 1 0.0000 0.996 1.000 0.000
#> SRR1353376 2 0.7299 0.882 0.204 0.796
#> SRR1499040 1 0.0000 0.996 1.000 0.000
#> SRR1322312 1 0.0000 0.996 1.000 0.000
#> SRR1324412 1 0.0000 0.996 1.000 0.000
#> SRR1100991 1 0.0000 0.996 1.000 0.000
#> SRR1349479 2 0.8327 0.832 0.264 0.736
#> SRR1431248 1 0.0000 0.996 1.000 0.000
#> SRR1405054 1 0.0000 0.996 1.000 0.000
#> SRR1312266 1 0.0000 0.996 1.000 0.000
#> SRR1409790 1 0.0000 0.996 1.000 0.000
#> SRR1352507 1 0.0000 0.996 1.000 0.000
#> SRR1383763 1 0.0000 0.996 1.000 0.000
#> SRR1468314 1 0.0376 0.992 0.996 0.004
#> SRR1473674 1 0.0376 0.992 0.996 0.004
#> SRR1390499 1 0.0000 0.996 1.000 0.000
#> SRR821043 2 0.0000 0.835 0.000 1.000
#> SRR1455653 2 0.0000 0.835 0.000 1.000
#> SRR1335236 1 0.0376 0.992 0.996 0.004
#> SRR1095383 2 0.7453 0.879 0.212 0.788
#> SRR1479489 1 0.0000 0.996 1.000 0.000
#> SRR1310433 1 0.0376 0.992 0.996 0.004
#> SRR1073435 1 0.0000 0.996 1.000 0.000
#> SRR659649 1 0.0000 0.996 1.000 0.000
#> SRR1395999 1 0.0000 0.996 1.000 0.000
#> SRR1105248 2 0.6887 0.882 0.184 0.816
#> SRR1338257 1 0.0000 0.996 1.000 0.000
#> SRR1499395 1 0.0000 0.996 1.000 0.000
#> SRR1350002 1 0.0376 0.992 0.996 0.004
#> SRR1489757 1 0.0000 0.996 1.000 0.000
#> SRR1414637 1 0.0000 0.996 1.000 0.000
#> SRR1478113 2 0.7139 0.883 0.196 0.804
#> SRR1322477 1 0.1633 0.970 0.976 0.024
#> SRR1478789 1 0.0000 0.996 1.000 0.000
#> SRR1414185 1 0.5178 0.842 0.884 0.116
#> SRR1069141 1 0.0376 0.992 0.996 0.004
#> SRR1376852 1 0.0000 0.996 1.000 0.000
#> SRR1323491 1 0.0000 0.996 1.000 0.000
#> SRR1338103 1 0.0000 0.996 1.000 0.000
#> SRR1472012 1 0.0000 0.996 1.000 0.000
#> SRR1340325 1 0.0000 0.996 1.000 0.000
#> SRR1087321 1 0.0000 0.996 1.000 0.000
#> SRR1488790 1 0.0000 0.996 1.000 0.000
#> SRR1334866 1 0.0000 0.996 1.000 0.000
#> SRR1089446 1 0.0000 0.996 1.000 0.000
#> SRR1344445 1 0.0000 0.996 1.000 0.000
#> SRR1412969 1 0.0672 0.988 0.992 0.008
#> SRR1071668 1 0.0000 0.996 1.000 0.000
#> SRR1075804 1 0.1633 0.970 0.976 0.024
#> SRR1383283 1 0.0000 0.996 1.000 0.000
#> SRR1350239 2 0.8016 0.856 0.244 0.756
#> SRR1353878 1 0.0000 0.996 1.000 0.000
#> SRR1375721 1 0.0000 0.996 1.000 0.000
#> SRR1083983 1 0.0000 0.996 1.000 0.000
#> SRR1090095 1 0.0000 0.996 1.000 0.000
#> SRR1414792 1 0.0000 0.996 1.000 0.000
#> SRR1075102 2 0.7139 0.883 0.196 0.804
#> SRR1098737 1 0.1633 0.970 0.976 0.024
#> SRR1349409 1 0.0000 0.996 1.000 0.000
#> SRR1413008 2 0.8016 0.856 0.244 0.756
#> SRR1407179 1 0.0000 0.996 1.000 0.000
#> SRR1095913 1 0.0000 0.996 1.000 0.000
#> SRR1403544 1 0.0000 0.996 1.000 0.000
#> SRR1490546 1 0.0000 0.996 1.000 0.000
#> SRR807971 1 0.0000 0.996 1.000 0.000
#> SRR1436228 1 0.0000 0.996 1.000 0.000
#> SRR1445218 1 0.0376 0.992 0.996 0.004
#> SRR1485438 1 0.0000 0.996 1.000 0.000
#> SRR1358143 1 0.0000 0.996 1.000 0.000
#> SRR1328760 1 0.0000 0.996 1.000 0.000
#> SRR1380806 1 0.0000 0.996 1.000 0.000
#> SRR1379426 1 0.5178 0.842 0.884 0.116
#> SRR1087007 1 0.0000 0.996 1.000 0.000
#> SRR1086256 1 0.0376 0.992 0.996 0.004
#> SRR1346734 2 0.0000 0.835 0.000 1.000
#> SRR1414515 1 0.0000 0.996 1.000 0.000
#> SRR1082151 1 0.0000 0.996 1.000 0.000
#> SRR1349320 2 0.7139 0.883 0.196 0.804
#> SRR1317554 2 0.0376 0.836 0.004 0.996
#> SRR1076022 1 0.0376 0.992 0.996 0.004
#> SRR1339573 1 0.0000 0.996 1.000 0.000
#> SRR1455878 1 0.0000 0.996 1.000 0.000
#> SRR1446203 1 0.0000 0.996 1.000 0.000
#> SRR1387397 1 0.0000 0.996 1.000 0.000
#> SRR1402590 1 0.0000 0.996 1.000 0.000
#> SRR1317532 1 0.1633 0.970 0.976 0.024
#> SRR1331488 2 0.7950 0.859 0.240 0.760
#> SRR1499675 1 0.0000 0.996 1.000 0.000
#> SRR1440467 1 0.0672 0.988 0.992 0.008
#> SRR807995 1 0.0376 0.992 0.996 0.004
#> SRR1476485 2 0.0000 0.835 0.000 1.000
#> SRR1388214 1 0.0000 0.996 1.000 0.000
#> SRR1456051 1 0.0000 0.996 1.000 0.000
#> SRR1473275 1 0.0000 0.996 1.000 0.000
#> SRR1444083 1 0.0000 0.996 1.000 0.000
#> SRR1313807 1 0.0000 0.996 1.000 0.000
#> SRR1470751 1 0.0000 0.996 1.000 0.000
#> SRR1403434 1 0.0672 0.988 0.992 0.008
#> SRR1390540 1 0.0000 0.996 1.000 0.000
#> SRR1093861 1 0.0376 0.992 0.996 0.004
#> SRR1325290 1 0.0000 0.996 1.000 0.000
#> SRR1070689 1 0.0000 0.996 1.000 0.000
#> SRR1384049 1 0.0000 0.996 1.000 0.000
#> SRR1081184 1 0.0000 0.996 1.000 0.000
#> SRR1324295 1 0.0000 0.996 1.000 0.000
#> SRR1365313 1 0.0000 0.996 1.000 0.000
#> SRR1321877 1 0.0000 0.996 1.000 0.000
#> SRR815711 1 0.0000 0.996 1.000 0.000
#> SRR1433476 2 0.9427 0.670 0.360 0.640
#> SRR1101883 1 0.0000 0.996 1.000 0.000
#> SRR1433729 2 0.7453 0.879 0.212 0.788
#> SRR1341877 1 0.0000 0.996 1.000 0.000
#> SRR1090556 1 0.0000 0.996 1.000 0.000
#> SRR1357389 1 0.0000 0.996 1.000 0.000
#> SRR1404227 1 0.0000 0.996 1.000 0.000
#> SRR1376830 1 0.0000 0.996 1.000 0.000
#> SRR1500661 1 0.0000 0.996 1.000 0.000
#> SRR1080294 2 0.7453 0.879 0.212 0.788
#> SRR1336314 2 0.0000 0.835 0.000 1.000
#> SRR1102152 1 0.0000 0.996 1.000 0.000
#> SRR1345244 1 0.0000 0.996 1.000 0.000
#> SRR1478637 1 0.0000 0.996 1.000 0.000
#> SRR1443776 1 0.0000 0.996 1.000 0.000
#> SRR1120939 1 0.0000 0.996 1.000 0.000
#> SRR1080117 1 0.0000 0.996 1.000 0.000
#> SRR1102899 1 0.1184 0.980 0.984 0.016
#> SRR1091865 1 0.0000 0.996 1.000 0.000
#> SRR1361072 1 0.0000 0.996 1.000 0.000
#> SRR1487890 1 0.0000 0.996 1.000 0.000
#> SRR1349456 1 0.0000 0.996 1.000 0.000
#> SRR1389384 1 0.0000 0.996 1.000 0.000
#> SRR1316096 1 0.0376 0.992 0.996 0.004
#> SRR1408512 1 0.0000 0.996 1.000 0.000
#> SRR1447547 2 0.8016 0.856 0.244 0.756
#> SRR1354053 2 0.0000 0.835 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1349562 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1353376 2 0.2492 0.881 0.016 0.936 0.048
#> SRR1499040 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1322312 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1324412 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1100991 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1349479 2 0.1182 0.853 0.012 0.976 0.012
#> SRR1431248 1 0.0475 0.956 0.992 0.004 0.004
#> SRR1405054 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1312266 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1409790 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1352507 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1383763 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1468314 1 0.3784 0.874 0.864 0.132 0.004
#> SRR1473674 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1390499 1 0.0237 0.956 0.996 0.000 0.004
#> SRR821043 3 0.0592 0.939 0.000 0.012 0.988
#> SRR1455653 3 0.3686 0.869 0.000 0.140 0.860
#> SRR1335236 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1095383 2 0.2176 0.883 0.020 0.948 0.032
#> SRR1479489 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1310433 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1073435 1 0.2301 0.932 0.936 0.060 0.004
#> SRR659649 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1395999 1 0.0237 0.957 0.996 0.004 0.000
#> SRR1105248 2 0.2902 0.875 0.016 0.920 0.064
#> SRR1338257 1 0.0475 0.956 0.992 0.004 0.004
#> SRR1499395 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1350002 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1489757 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1414637 1 0.2796 0.910 0.908 0.092 0.000
#> SRR1478113 2 0.3610 0.875 0.016 0.888 0.096
#> SRR1322477 1 0.1647 0.943 0.960 0.036 0.004
#> SRR1478789 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1414185 1 0.4293 0.819 0.832 0.164 0.004
#> SRR1069141 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1376852 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1323491 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1338103 1 0.1031 0.953 0.976 0.024 0.000
#> SRR1472012 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1340325 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1087321 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1488790 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1334866 1 0.2261 0.927 0.932 0.068 0.000
#> SRR1089446 1 0.5115 0.773 0.768 0.228 0.004
#> SRR1344445 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1412969 1 0.2680 0.922 0.924 0.068 0.008
#> SRR1071668 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1075804 1 0.1267 0.945 0.972 0.024 0.004
#> SRR1383283 1 0.2496 0.926 0.928 0.068 0.004
#> SRR1350239 2 0.2846 0.875 0.020 0.924 0.056
#> SRR1353878 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1375721 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1083983 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1090095 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1414792 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1075102 2 0.3995 0.865 0.016 0.868 0.116
#> SRR1098737 1 0.1267 0.945 0.972 0.024 0.004
#> SRR1349409 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1413008 2 0.2846 0.875 0.020 0.924 0.056
#> SRR1407179 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1095913 1 0.3030 0.908 0.904 0.092 0.004
#> SRR1403544 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1490546 1 0.0237 0.956 0.996 0.000 0.004
#> SRR807971 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1436228 1 0.0747 0.956 0.984 0.016 0.000
#> SRR1445218 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1485438 1 0.1643 0.944 0.956 0.044 0.000
#> SRR1358143 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1380806 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1379426 1 0.4047 0.833 0.848 0.148 0.004
#> SRR1087007 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1086256 1 0.2711 0.913 0.912 0.088 0.000
#> SRR1346734 3 0.0747 0.940 0.000 0.016 0.984
#> SRR1414515 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1082151 1 0.1525 0.947 0.964 0.032 0.004
#> SRR1349320 2 0.3610 0.875 0.016 0.888 0.096
#> SRR1317554 2 0.5058 0.568 0.000 0.756 0.244
#> SRR1076022 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1339573 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1455878 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1446203 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1387397 1 0.0747 0.956 0.984 0.016 0.000
#> SRR1402590 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1317532 1 0.1267 0.945 0.972 0.024 0.004
#> SRR1331488 2 0.6283 0.591 0.176 0.760 0.064
#> SRR1499675 1 0.1163 0.950 0.972 0.028 0.000
#> SRR1440467 1 0.5461 0.746 0.748 0.244 0.008
#> SRR807995 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1476485 3 0.0747 0.940 0.000 0.016 0.984
#> SRR1388214 1 0.0661 0.956 0.988 0.008 0.004
#> SRR1456051 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1473275 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1444083 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1313807 1 0.2496 0.926 0.928 0.068 0.004
#> SRR1470751 1 0.1525 0.947 0.964 0.032 0.004
#> SRR1403434 1 0.5461 0.746 0.748 0.244 0.008
#> SRR1390540 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1093861 1 0.4409 0.834 0.824 0.172 0.004
#> SRR1325290 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1070689 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1384049 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1081184 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1324295 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1365313 1 0.0892 0.954 0.980 0.020 0.000
#> SRR1321877 1 0.0747 0.955 0.984 0.016 0.000
#> SRR815711 1 0.5115 0.773 0.768 0.228 0.004
#> SRR1433476 2 0.3695 0.690 0.108 0.880 0.012
#> SRR1101883 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1433729 2 0.2313 0.882 0.024 0.944 0.032
#> SRR1341877 1 0.1031 0.953 0.976 0.024 0.000
#> SRR1090556 1 0.0475 0.956 0.992 0.004 0.004
#> SRR1357389 1 0.0892 0.955 0.980 0.020 0.000
#> SRR1404227 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1376830 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1500661 1 0.0000 0.956 1.000 0.000 0.000
#> SRR1080294 2 0.2313 0.882 0.024 0.944 0.032
#> SRR1336314 3 0.0747 0.938 0.000 0.016 0.984
#> SRR1102152 1 0.2400 0.927 0.932 0.064 0.004
#> SRR1345244 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1478637 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1443776 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1120939 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1080117 1 0.0747 0.955 0.984 0.016 0.000
#> SRR1102899 1 0.4784 0.804 0.796 0.200 0.004
#> SRR1091865 1 0.0829 0.955 0.984 0.012 0.004
#> SRR1361072 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1487890 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1349456 1 0.0592 0.956 0.988 0.012 0.000
#> SRR1389384 1 0.0475 0.956 0.992 0.004 0.004
#> SRR1316096 1 0.4629 0.818 0.808 0.188 0.004
#> SRR1408512 1 0.0237 0.957 0.996 0.004 0.000
#> SRR1447547 2 0.2384 0.863 0.008 0.936 0.056
#> SRR1354053 3 0.3686 0.869 0.000 0.140 0.860
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1349562 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1353376 4 0.5793 0.6082 0.012 0.012 0.448 0.528
#> SRR1499040 1 0.0524 0.7538 0.988 0.008 0.000 0.004
#> SRR1322312 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1324412 1 0.1124 0.7528 0.972 0.012 0.012 0.004
#> SRR1100991 1 0.1124 0.7528 0.972 0.012 0.012 0.004
#> SRR1349479 3 0.4456 0.3486 0.004 0.000 0.716 0.280
#> SRR1431248 1 0.4608 0.7448 0.692 0.304 0.000 0.004
#> SRR1405054 1 0.3801 0.7579 0.780 0.220 0.000 0.000
#> SRR1312266 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1409790 1 0.1124 0.7528 0.972 0.012 0.012 0.004
#> SRR1352507 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1383763 1 0.4304 0.7492 0.716 0.284 0.000 0.000
#> SRR1468314 1 0.4406 0.4709 0.700 0.000 0.000 0.300
#> SRR1473674 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1390499 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR821043 2 0.5646 0.9286 0.000 0.672 0.272 0.056
#> SRR1455653 2 0.6661 0.8436 0.000 0.604 0.264 0.132
#> SRR1335236 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1095383 4 0.5383 0.6126 0.012 0.000 0.452 0.536
#> SRR1479489 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1310433 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1073435 1 0.2530 0.6910 0.888 0.000 0.000 0.112
#> SRR659649 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1395999 1 0.4560 0.7466 0.700 0.296 0.000 0.004
#> SRR1105248 4 0.6176 0.5890 0.012 0.028 0.452 0.508
#> SRR1338257 1 0.4741 0.7320 0.668 0.328 0.000 0.004
#> SRR1499395 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1350002 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1489757 1 0.1124 0.7528 0.972 0.012 0.012 0.004
#> SRR1414637 1 0.2976 0.6879 0.872 0.008 0.000 0.120
#> SRR1478113 4 0.5497 0.6083 0.012 0.020 0.300 0.668
#> SRR1322477 1 0.5496 0.7244 0.652 0.312 0.000 0.036
#> SRR1478789 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1414185 1 0.4287 0.6598 0.828 0.004 0.080 0.088
#> SRR1069141 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1376852 1 0.4454 0.7426 0.692 0.308 0.000 0.000
#> SRR1323491 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1338103 1 0.3224 0.7611 0.864 0.120 0.000 0.016
#> SRR1472012 1 0.0524 0.7538 0.988 0.008 0.000 0.004
#> SRR1340325 1 0.4477 0.7416 0.688 0.312 0.000 0.000
#> SRR1087321 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1488790 1 0.4522 0.7371 0.680 0.320 0.000 0.000
#> SRR1334866 1 0.2480 0.7119 0.904 0.008 0.000 0.088
#> SRR1089446 1 0.4302 0.5593 0.756 0.004 0.236 0.004
#> SRR1344445 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1412969 1 0.2408 0.7222 0.920 0.004 0.060 0.016
#> SRR1071668 1 0.1124 0.7528 0.972 0.012 0.012 0.004
#> SRR1075804 1 0.5250 0.7241 0.660 0.316 0.000 0.024
#> SRR1383283 1 0.2704 0.6810 0.876 0.000 0.000 0.124
#> SRR1350239 4 0.5375 0.4358 0.008 0.004 0.416 0.572
#> SRR1353878 1 0.4406 0.7456 0.700 0.300 0.000 0.000
#> SRR1375721 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1083983 1 0.0524 0.7538 0.988 0.008 0.000 0.004
#> SRR1090095 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1414792 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1075102 4 0.5662 0.5844 0.012 0.024 0.312 0.652
#> SRR1098737 1 0.5250 0.7241 0.660 0.316 0.000 0.024
#> SRR1349409 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1413008 4 0.5375 0.4358 0.008 0.004 0.416 0.572
#> SRR1407179 1 0.0188 0.7517 0.996 0.000 0.000 0.004
#> SRR1095913 1 0.3074 0.6551 0.848 0.000 0.000 0.152
#> SRR1403544 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1490546 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR807971 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1436228 1 0.2799 0.7616 0.884 0.108 0.000 0.008
#> SRR1445218 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1485438 1 0.4134 0.5274 0.740 0.000 0.000 0.260
#> SRR1358143 1 0.4304 0.7492 0.716 0.284 0.000 0.000
#> SRR1328760 1 0.4406 0.7456 0.700 0.300 0.000 0.000
#> SRR1380806 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1379426 1 0.4004 0.6745 0.844 0.004 0.064 0.088
#> SRR1087007 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1086256 1 0.2918 0.6911 0.876 0.008 0.000 0.116
#> SRR1346734 2 0.5716 0.9293 0.000 0.668 0.272 0.060
#> SRR1414515 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1082151 1 0.5343 0.7445 0.708 0.240 0.000 0.052
#> SRR1349320 4 0.5497 0.6083 0.012 0.020 0.300 0.668
#> SRR1317554 3 0.7544 -0.0398 0.000 0.196 0.452 0.352
#> SRR1076022 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1339573 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1455878 1 0.4454 0.7426 0.692 0.308 0.000 0.000
#> SRR1446203 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1387397 1 0.2799 0.7616 0.884 0.108 0.000 0.008
#> SRR1402590 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1317532 1 0.5250 0.7241 0.660 0.316 0.000 0.024
#> SRR1331488 4 0.7856 0.0333 0.144 0.032 0.300 0.524
#> SRR1499675 1 0.1356 0.7479 0.960 0.008 0.032 0.000
#> SRR1440467 1 0.4664 0.5304 0.736 0.004 0.248 0.012
#> SRR807995 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1476485 2 0.5716 0.9293 0.000 0.668 0.272 0.060
#> SRR1388214 1 0.4877 0.7310 0.664 0.328 0.000 0.008
#> SRR1456051 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1473275 1 0.0188 0.7517 0.996 0.000 0.000 0.004
#> SRR1444083 1 0.4500 0.7395 0.684 0.316 0.000 0.000
#> SRR1313807 1 0.2704 0.6810 0.876 0.000 0.000 0.124
#> SRR1470751 1 0.5343 0.7445 0.708 0.240 0.000 0.052
#> SRR1403434 1 0.4664 0.5304 0.736 0.004 0.248 0.012
#> SRR1390540 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1093861 1 0.4992 0.1348 0.524 0.000 0.000 0.476
#> SRR1325290 1 0.0524 0.7538 0.988 0.008 0.000 0.004
#> SRR1070689 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1384049 1 0.4304 0.7492 0.716 0.284 0.000 0.000
#> SRR1081184 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1324295 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1365313 1 0.0469 0.7494 0.988 0.000 0.000 0.012
#> SRR1321877 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR815711 1 0.4302 0.5593 0.756 0.004 0.236 0.004
#> SRR1433476 3 0.6468 0.2397 0.096 0.004 0.624 0.276
#> SRR1101883 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1433729 4 0.5488 0.6135 0.016 0.000 0.452 0.532
#> SRR1341877 1 0.3224 0.7611 0.864 0.120 0.000 0.016
#> SRR1090556 1 0.4608 0.7448 0.692 0.304 0.000 0.004
#> SRR1357389 1 0.0524 0.7492 0.988 0.000 0.008 0.004
#> SRR1404227 1 0.0188 0.7517 0.996 0.000 0.000 0.004
#> SRR1376830 1 0.4454 0.7426 0.692 0.308 0.000 0.000
#> SRR1500661 1 0.4304 0.7492 0.716 0.284 0.000 0.000
#> SRR1080294 4 0.5488 0.6135 0.016 0.000 0.452 0.532
#> SRR1336314 2 0.5716 0.9290 0.000 0.668 0.272 0.060
#> SRR1102152 1 0.6238 0.7076 0.632 0.276 0.000 0.092
#> SRR1345244 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1478637 1 0.0524 0.7538 0.988 0.008 0.000 0.004
#> SRR1443776 1 0.0188 0.7514 0.996 0.000 0.000 0.004
#> SRR1120939 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1080117 1 0.0336 0.7503 0.992 0.000 0.000 0.008
#> SRR1102899 1 0.5296 0.0722 0.500 0.000 0.008 0.492
#> SRR1091865 1 0.4872 0.7506 0.728 0.244 0.000 0.028
#> SRR1361072 1 0.4564 0.7326 0.672 0.328 0.000 0.000
#> SRR1487890 1 0.4477 0.7410 0.688 0.312 0.000 0.000
#> SRR1349456 1 0.0817 0.7449 0.976 0.000 0.000 0.024
#> SRR1389384 1 0.4327 0.7563 0.768 0.216 0.000 0.016
#> SRR1316096 1 0.4999 0.0975 0.508 0.000 0.000 0.492
#> SRR1408512 1 0.4560 0.7466 0.700 0.296 0.000 0.004
#> SRR1447547 3 0.5080 0.2444 0.000 0.004 0.576 0.420
#> SRR1354053 2 0.6661 0.8436 0.000 0.604 0.264 0.132
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1349562 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 3 0.4114 0.69708 0.000 0.000 0.624 0.000 0.376
#> SRR1499040 1 0.4074 0.53680 0.636 0.364 0.000 0.000 0.000
#> SRR1322312 1 0.0162 0.70935 0.996 0.004 0.000 0.000 0.000
#> SRR1324412 1 0.4570 0.53377 0.632 0.348 0.020 0.000 0.000
#> SRR1100991 1 0.4570 0.53377 0.632 0.348 0.020 0.000 0.000
#> SRR1349479 3 0.0000 0.56238 0.000 0.000 1.000 0.000 0.000
#> SRR1431248 1 0.1281 0.70826 0.956 0.032 0.000 0.000 0.012
#> SRR1405054 1 0.2377 0.68103 0.872 0.128 0.000 0.000 0.000
#> SRR1312266 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1409790 1 0.4570 0.53377 0.632 0.348 0.020 0.000 0.000
#> SRR1352507 1 0.4354 0.52454 0.624 0.368 0.008 0.000 0.000
#> SRR1383763 1 0.0880 0.71002 0.968 0.032 0.000 0.000 0.000
#> SRR1468314 2 0.4121 0.54929 0.208 0.760 0.008 0.000 0.024
#> SRR1473674 2 0.0794 0.59738 0.000 0.972 0.000 0.000 0.028
#> SRR1390499 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.92895 0.000 0.000 0.000 1.000 0.000
#> SRR1455653 4 0.2964 0.85858 0.000 0.000 0.120 0.856 0.024
#> SRR1335236 2 0.0404 0.59750 0.000 0.988 0.000 0.000 0.012
#> SRR1095383 3 0.5359 0.75359 0.000 0.100 0.644 0.000 0.256
#> SRR1479489 1 0.0162 0.70935 0.996 0.004 0.000 0.000 0.000
#> SRR1310433 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1073435 2 0.4684 -0.05007 0.452 0.536 0.004 0.000 0.008
#> SRR659649 1 0.4354 0.52454 0.624 0.368 0.008 0.000 0.000
#> SRR1395999 1 0.1364 0.70905 0.952 0.036 0.000 0.000 0.012
#> SRR1105248 3 0.4759 0.71973 0.000 0.004 0.636 0.024 0.336
#> SRR1338257 1 0.0609 0.69947 0.980 0.000 0.000 0.000 0.020
#> SRR1499395 1 0.4380 0.51417 0.616 0.376 0.008 0.000 0.000
#> SRR1350002 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1489757 1 0.4570 0.53377 0.632 0.348 0.020 0.000 0.000
#> SRR1414637 2 0.4747 -0.17523 0.484 0.500 0.000 0.000 0.016
#> SRR1478113 5 0.0609 0.74217 0.000 0.000 0.000 0.020 0.980
#> SRR1322477 1 0.1626 0.68431 0.940 0.016 0.000 0.000 0.044
#> SRR1478789 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1414185 1 0.6814 0.32445 0.524 0.304 0.040 0.000 0.132
#> SRR1069141 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1376852 1 0.0162 0.70968 0.996 0.004 0.000 0.000 0.000
#> SRR1323491 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1338103 1 0.3534 0.61555 0.744 0.256 0.000 0.000 0.000
#> SRR1472012 1 0.4074 0.53680 0.636 0.364 0.000 0.000 0.000
#> SRR1340325 1 0.0324 0.70917 0.992 0.004 0.000 0.000 0.004
#> SRR1087321 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1488790 1 0.0290 0.70536 0.992 0.000 0.000 0.000 0.008
#> SRR1334866 1 0.4740 0.25008 0.516 0.468 0.000 0.000 0.016
#> SRR1089446 1 0.6685 0.03466 0.416 0.340 0.244 0.000 0.000
#> SRR1344445 1 0.4354 0.52454 0.624 0.368 0.008 0.000 0.000
#> SRR1412969 1 0.5634 0.43384 0.568 0.352 0.076 0.000 0.004
#> SRR1071668 1 0.4570 0.53377 0.632 0.348 0.020 0.000 0.000
#> SRR1075804 1 0.1121 0.68869 0.956 0.000 0.000 0.000 0.044
#> SRR1383283 2 0.4670 0.00241 0.440 0.548 0.004 0.000 0.008
#> SRR1350239 5 0.3648 0.73822 0.000 0.004 0.188 0.016 0.792
#> SRR1353878 1 0.0992 0.71019 0.968 0.024 0.000 0.000 0.008
#> SRR1375721 1 0.0162 0.70935 0.996 0.004 0.000 0.000 0.000
#> SRR1083983 1 0.4074 0.53680 0.636 0.364 0.000 0.000 0.000
#> SRR1090095 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1414792 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1075102 5 0.1043 0.73320 0.000 0.000 0.000 0.040 0.960
#> SRR1098737 1 0.1121 0.68869 0.956 0.000 0.000 0.000 0.044
#> SRR1349409 1 0.0162 0.70935 0.996 0.004 0.000 0.000 0.000
#> SRR1413008 5 0.3648 0.73822 0.000 0.004 0.188 0.016 0.792
#> SRR1407179 1 0.4138 0.51226 0.616 0.384 0.000 0.000 0.000
#> SRR1095913 2 0.4806 0.11027 0.408 0.572 0.004 0.000 0.016
#> SRR1403544 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR807971 1 0.4354 0.52454 0.624 0.368 0.008 0.000 0.000
#> SRR1436228 1 0.3586 0.61164 0.736 0.264 0.000 0.000 0.000
#> SRR1445218 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1485438 2 0.3561 0.47371 0.260 0.740 0.000 0.000 0.000
#> SRR1358143 1 0.0963 0.70975 0.964 0.036 0.000 0.000 0.000
#> SRR1328760 1 0.0992 0.71019 0.968 0.024 0.000 0.000 0.008
#> SRR1380806 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 1 0.6548 0.35475 0.536 0.308 0.024 0.000 0.132
#> SRR1087007 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1086256 2 0.4747 -0.19128 0.488 0.496 0.000 0.000 0.016
#> SRR1346734 4 0.0510 0.93075 0.000 0.000 0.000 0.984 0.016
#> SRR1414515 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 1 0.3039 0.64704 0.836 0.152 0.000 0.000 0.012
#> SRR1349320 5 0.0609 0.74217 0.000 0.000 0.000 0.020 0.980
#> SRR1317554 3 0.5631 0.63736 0.000 0.000 0.636 0.200 0.164
#> SRR1076022 2 0.0404 0.59750 0.000 0.988 0.000 0.000 0.012
#> SRR1339573 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1455878 1 0.0162 0.70968 0.996 0.004 0.000 0.000 0.000
#> SRR1446203 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1387397 1 0.3586 0.61164 0.736 0.264 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.1121 0.68869 0.956 0.000 0.000 0.000 0.044
#> SRR1331488 5 0.2806 0.46408 0.152 0.004 0.000 0.000 0.844
#> SRR1499675 1 0.4822 0.51474 0.616 0.352 0.032 0.000 0.000
#> SRR1440467 1 0.6743 -0.02532 0.396 0.340 0.264 0.000 0.000
#> SRR807995 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1476485 4 0.0290 0.92955 0.000 0.000 0.000 0.992 0.008
#> SRR1388214 1 0.0771 0.69741 0.976 0.004 0.000 0.000 0.020
#> SRR1456051 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 1 0.4138 0.51226 0.616 0.384 0.000 0.000 0.000
#> SRR1444083 1 0.0693 0.70713 0.980 0.008 0.000 0.000 0.012
#> SRR1313807 2 0.4670 0.00241 0.440 0.548 0.004 0.000 0.008
#> SRR1470751 1 0.3039 0.64704 0.836 0.152 0.000 0.000 0.012
#> SRR1403434 1 0.6743 -0.02532 0.396 0.340 0.264 0.000 0.000
#> SRR1390540 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1093861 2 0.0693 0.60192 0.012 0.980 0.000 0.000 0.008
#> SRR1325290 1 0.4074 0.53680 0.636 0.364 0.000 0.000 0.000
#> SRR1070689 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0963 0.70975 0.964 0.036 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 1 0.4161 0.49944 0.608 0.392 0.000 0.000 0.000
#> SRR1321877 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR815711 1 0.6685 0.03466 0.416 0.340 0.244 0.000 0.000
#> SRR1433476 3 0.1965 0.41870 0.096 0.000 0.904 0.000 0.000
#> SRR1101883 1 0.4354 0.52454 0.624 0.368 0.008 0.000 0.000
#> SRR1433729 3 0.5382 0.75299 0.000 0.104 0.644 0.000 0.252
#> SRR1341877 1 0.3534 0.61555 0.744 0.256 0.000 0.000 0.000
#> SRR1090556 1 0.1195 0.70816 0.960 0.028 0.000 0.000 0.012
#> SRR1357389 1 0.4525 0.52506 0.624 0.360 0.016 0.000 0.000
#> SRR1404227 1 0.4138 0.51226 0.616 0.384 0.000 0.000 0.000
#> SRR1376830 1 0.0162 0.70968 0.996 0.004 0.000 0.000 0.000
#> SRR1500661 1 0.0880 0.71002 0.968 0.032 0.000 0.000 0.000
#> SRR1080294 3 0.5403 0.75022 0.000 0.108 0.644 0.000 0.248
#> SRR1336314 4 0.0404 0.93134 0.000 0.000 0.000 0.988 0.012
#> SRR1102152 1 0.3146 0.58384 0.844 0.128 0.000 0.000 0.028
#> SRR1345244 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1478637 1 0.4074 0.53680 0.636 0.364 0.000 0.000 0.000
#> SRR1443776 1 0.4276 0.51358 0.616 0.380 0.004 0.000 0.000
#> SRR1120939 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1080117 1 0.4288 0.50763 0.612 0.384 0.004 0.000 0.000
#> SRR1102899 2 0.1484 0.57400 0.000 0.944 0.008 0.000 0.048
#> SRR1091865 1 0.2471 0.66971 0.864 0.136 0.000 0.000 0.000
#> SRR1361072 1 0.0510 0.70140 0.984 0.000 0.000 0.000 0.016
#> SRR1487890 1 0.0000 0.70853 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 1 0.4341 0.47170 0.592 0.404 0.004 0.000 0.000
#> SRR1389384 1 0.3010 0.65809 0.824 0.172 0.000 0.000 0.004
#> SRR1316096 2 0.0880 0.59616 0.000 0.968 0.000 0.000 0.032
#> SRR1408512 1 0.1364 0.70905 0.952 0.036 0.000 0.000 0.012
#> SRR1447547 5 0.4482 0.59435 0.000 0.000 0.348 0.016 0.636
#> SRR1354053 4 0.2964 0.85858 0.000 0.000 0.120 0.856 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0777 0.6847 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR1349562 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1353376 4 0.5217 0.5190 0.000 0.000 0.232 0.608 0.000 0.160
#> SRR1499040 1 0.3852 0.4780 0.612 0.384 0.000 0.004 0.000 0.000
#> SRR1322312 1 0.0291 0.6974 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1324412 1 0.4121 0.4669 0.604 0.380 0.000 0.000 0.000 0.016
#> SRR1100991 1 0.4121 0.4669 0.604 0.380 0.000 0.000 0.000 0.016
#> SRR1349479 6 0.3586 0.8142 0.000 0.004 0.004 0.280 0.000 0.712
#> SRR1431248 1 0.1624 0.6936 0.936 0.040 0.000 0.004 0.000 0.020
#> SRR1405054 1 0.2300 0.6548 0.856 0.144 0.000 0.000 0.000 0.000
#> SRR1312266 1 0.0777 0.6887 0.972 0.004 0.000 0.000 0.000 0.024
#> SRR1409790 1 0.4121 0.4669 0.604 0.380 0.000 0.000 0.000 0.016
#> SRR1352507 1 0.3890 0.4587 0.596 0.400 0.000 0.000 0.000 0.004
#> SRR1383763 1 0.0935 0.6969 0.964 0.032 0.000 0.004 0.000 0.000
#> SRR1468314 2 0.3875 0.5679 0.188 0.768 0.008 0.028 0.000 0.008
#> SRR1473674 2 0.1793 0.4855 0.000 0.928 0.004 0.032 0.000 0.036
#> SRR1390499 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR821043 5 0.0363 0.7945 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR1455653 5 0.5263 0.7699 0.000 0.000 0.012 0.248 0.624 0.116
#> SRR1335236 2 0.1391 0.5044 0.000 0.944 0.000 0.040 0.000 0.016
#> SRR1095383 4 0.3013 0.7970 0.000 0.068 0.088 0.844 0.000 0.000
#> SRR1479489 1 0.0291 0.6974 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1310433 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1073435 2 0.4635 0.0608 0.432 0.536 0.004 0.024 0.000 0.004
#> SRR659649 1 0.3890 0.4587 0.596 0.400 0.000 0.000 0.000 0.004
#> SRR1395999 1 0.1464 0.6952 0.944 0.036 0.000 0.004 0.000 0.016
#> SRR1105248 4 0.2882 0.7296 0.000 0.000 0.180 0.812 0.008 0.000
#> SRR1338257 1 0.0777 0.6849 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR1499395 1 0.3907 0.4466 0.588 0.408 0.000 0.000 0.000 0.004
#> SRR1350002 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1489757 1 0.4121 0.4669 0.604 0.380 0.000 0.000 0.000 0.016
#> SRR1414637 2 0.4950 -0.0795 0.468 0.484 0.008 0.036 0.000 0.004
#> SRR1478113 3 0.0547 0.7622 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1322477 1 0.2109 0.6718 0.920 0.024 0.024 0.004 0.000 0.028
#> SRR1478789 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1414185 1 0.6285 0.2049 0.496 0.336 0.128 0.008 0.000 0.032
#> SRR1069141 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1376852 1 0.0291 0.6977 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1323491 1 0.0777 0.6847 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR1338103 1 0.3405 0.5705 0.724 0.272 0.000 0.004 0.000 0.000
#> SRR1472012 1 0.3852 0.4780 0.612 0.384 0.000 0.004 0.000 0.000
#> SRR1340325 1 0.0405 0.6970 0.988 0.004 0.000 0.000 0.000 0.008
#> SRR1087321 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1488790 1 0.0405 0.6936 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR1334866 1 0.4886 0.1714 0.500 0.456 0.008 0.032 0.000 0.004
#> SRR1089446 2 0.6116 0.3383 0.304 0.364 0.000 0.000 0.000 0.332
#> SRR1344445 1 0.3890 0.4587 0.596 0.400 0.000 0.000 0.000 0.004
#> SRR1412969 1 0.5336 0.2759 0.516 0.384 0.004 0.000 0.000 0.096
#> SRR1071668 1 0.4121 0.4669 0.604 0.380 0.000 0.000 0.000 0.016
#> SRR1075804 1 0.1341 0.6736 0.948 0.000 0.024 0.000 0.000 0.028
#> SRR1383283 2 0.4619 0.1094 0.420 0.548 0.004 0.024 0.000 0.004
#> SRR1350239 3 0.2946 0.7233 0.000 0.000 0.812 0.012 0.000 0.176
#> SRR1353878 1 0.1088 0.6967 0.960 0.024 0.000 0.000 0.000 0.016
#> SRR1375721 1 0.0291 0.6974 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1083983 1 0.3852 0.4780 0.612 0.384 0.000 0.004 0.000 0.000
#> SRR1090095 1 0.0777 0.6847 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR1414792 1 0.0777 0.6847 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR1075102 3 0.1092 0.7535 0.000 0.000 0.960 0.020 0.020 0.000
#> SRR1098737 1 0.1700 0.6780 0.936 0.012 0.024 0.000 0.000 0.028
#> SRR1349409 1 0.0291 0.6974 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1413008 3 0.2946 0.7233 0.000 0.000 0.812 0.012 0.000 0.176
#> SRR1407179 1 0.3774 0.4503 0.592 0.408 0.000 0.000 0.000 0.000
#> SRR1095913 2 0.4860 0.2082 0.388 0.564 0.008 0.036 0.000 0.004
#> SRR1403544 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1490546 1 0.0777 0.6847 0.972 0.000 0.000 0.004 0.000 0.024
#> SRR807971 1 0.3890 0.4587 0.596 0.400 0.000 0.000 0.000 0.004
#> SRR1436228 1 0.3448 0.5673 0.716 0.280 0.000 0.004 0.000 0.000
#> SRR1445218 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1485438 2 0.3887 0.5095 0.248 0.724 0.000 0.008 0.000 0.020
#> SRR1358143 1 0.1010 0.6966 0.960 0.036 0.000 0.004 0.000 0.000
#> SRR1328760 1 0.1088 0.6967 0.960 0.024 0.000 0.000 0.000 0.016
#> SRR1380806 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1379426 1 0.6021 0.2449 0.508 0.340 0.128 0.008 0.000 0.016
#> SRR1087007 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1086256 2 0.4951 -0.0976 0.472 0.480 0.008 0.036 0.000 0.004
#> SRR1346734 5 0.2841 0.8167 0.000 0.000 0.012 0.128 0.848 0.012
#> SRR1414515 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1082151 1 0.2951 0.6225 0.820 0.168 0.004 0.004 0.000 0.004
#> SRR1349320 3 0.0547 0.7622 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1317554 4 0.1644 0.6171 0.000 0.000 0.004 0.920 0.076 0.000
#> SRR1076022 2 0.1391 0.5044 0.000 0.944 0.000 0.040 0.000 0.016
#> SRR1339573 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1455878 1 0.0146 0.6983 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR1446203 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1387397 1 0.3448 0.5673 0.716 0.280 0.000 0.004 0.000 0.000
#> SRR1402590 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1317532 1 0.1341 0.6736 0.948 0.000 0.024 0.000 0.000 0.028
#> SRR1331488 3 0.3219 0.4723 0.148 0.000 0.820 0.012 0.000 0.020
#> SRR1499675 1 0.4409 0.4419 0.588 0.380 0.000 0.000 0.000 0.032
#> SRR1440467 2 0.6100 0.3687 0.284 0.364 0.000 0.000 0.000 0.352
#> SRR807995 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1476485 5 0.1180 0.7818 0.000 0.000 0.012 0.016 0.960 0.012
#> SRR1388214 1 0.1036 0.6799 0.964 0.000 0.004 0.008 0.000 0.024
#> SRR1456051 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1473275 1 0.3774 0.4503 0.592 0.408 0.000 0.000 0.000 0.000
#> SRR1444083 1 0.0806 0.6926 0.972 0.008 0.000 0.000 0.000 0.020
#> SRR1313807 2 0.4619 0.1094 0.420 0.548 0.004 0.024 0.000 0.004
#> SRR1470751 1 0.2951 0.6225 0.820 0.168 0.004 0.004 0.000 0.004
#> SRR1403434 2 0.6100 0.3687 0.284 0.364 0.000 0.000 0.000 0.352
#> SRR1390540 1 0.0632 0.6867 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1093861 2 0.1820 0.5147 0.012 0.928 0.000 0.044 0.000 0.016
#> SRR1325290 1 0.3852 0.4780 0.612 0.384 0.000 0.004 0.000 0.000
#> SRR1070689 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1384049 1 0.1152 0.6956 0.952 0.044 0.000 0.004 0.000 0.000
#> SRR1081184 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1324295 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1365313 1 0.3782 0.4398 0.588 0.412 0.000 0.000 0.000 0.000
#> SRR1321877 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR815711 2 0.6116 0.3383 0.304 0.364 0.000 0.000 0.000 0.332
#> SRR1433476 6 0.3023 0.8358 0.000 0.008 0.004 0.180 0.000 0.808
#> SRR1101883 1 0.3890 0.4587 0.596 0.400 0.000 0.000 0.000 0.004
#> SRR1433729 4 0.3017 0.7954 0.000 0.072 0.084 0.844 0.000 0.000
#> SRR1341877 1 0.3405 0.5705 0.724 0.272 0.000 0.004 0.000 0.000
#> SRR1090556 1 0.1478 0.6942 0.944 0.032 0.000 0.004 0.000 0.020
#> SRR1357389 1 0.4150 0.4526 0.592 0.392 0.000 0.000 0.000 0.016
#> SRR1404227 1 0.3774 0.4503 0.592 0.408 0.000 0.000 0.000 0.000
#> SRR1376830 1 0.0291 0.6977 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR1500661 1 0.0935 0.6969 0.964 0.032 0.000 0.004 0.000 0.000
#> SRR1080294 4 0.2965 0.7922 0.000 0.072 0.080 0.848 0.000 0.000
#> SRR1336314 5 0.4266 0.8135 0.000 0.000 0.012 0.116 0.756 0.116
#> SRR1102152 1 0.2695 0.5684 0.844 0.144 0.004 0.008 0.000 0.000
#> SRR1345244 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1478637 1 0.3852 0.4780 0.612 0.384 0.000 0.004 0.000 0.000
#> SRR1443776 1 0.3782 0.4465 0.588 0.412 0.000 0.000 0.000 0.000
#> SRR1120939 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1080117 1 0.3789 0.4395 0.584 0.416 0.000 0.000 0.000 0.000
#> SRR1102899 2 0.2479 0.4878 0.000 0.892 0.028 0.064 0.000 0.016
#> SRR1091865 1 0.2442 0.6484 0.852 0.144 0.000 0.000 0.000 0.004
#> SRR1361072 1 0.0632 0.6867 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1487890 1 0.0146 0.6966 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1349456 1 0.3823 0.3972 0.564 0.436 0.000 0.000 0.000 0.000
#> SRR1389384 1 0.2979 0.6294 0.804 0.188 0.004 0.000 0.000 0.004
#> SRR1316096 2 0.1716 0.4830 0.000 0.932 0.004 0.028 0.000 0.036
#> SRR1408512 1 0.1464 0.6952 0.944 0.036 0.000 0.004 0.000 0.016
#> SRR1447547 3 0.3804 0.5062 0.000 0.000 0.656 0.008 0.000 0.336
#> SRR1354053 5 0.5263 0.7699 0.000 0.000 0.012 0.248 0.624 0.116
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.896 0.903 0.960 0.3033 0.707 0.707
#> 3 3 0.864 0.899 0.946 1.0087 0.609 0.475
#> 4 4 0.661 0.721 0.825 0.1521 0.893 0.732
#> 5 5 0.690 0.638 0.777 0.0815 0.939 0.808
#> 6 6 0.715 0.560 0.748 0.0551 0.893 0.629
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0672 0.9653 0.992 0.008
#> SRR1349562 1 0.0672 0.9653 0.992 0.008
#> SRR1353376 2 0.0000 0.9104 0.000 1.000
#> SRR1499040 1 0.0376 0.9646 0.996 0.004
#> SRR1322312 1 0.0672 0.9653 0.992 0.008
#> SRR1324412 1 0.0376 0.9646 0.996 0.004
#> SRR1100991 1 0.0376 0.9646 0.996 0.004
#> SRR1349479 2 0.0672 0.9066 0.008 0.992
#> SRR1431248 1 0.0672 0.9653 0.992 0.008
#> SRR1405054 1 0.0672 0.9653 0.992 0.008
#> SRR1312266 1 0.0672 0.9653 0.992 0.008
#> SRR1409790 1 0.0376 0.9646 0.996 0.004
#> SRR1352507 1 0.0672 0.9653 0.992 0.008
#> SRR1383763 1 0.0672 0.9653 0.992 0.008
#> SRR1468314 2 0.9922 0.2330 0.448 0.552
#> SRR1473674 1 0.8608 0.5794 0.716 0.284
#> SRR1390499 1 0.0672 0.9653 0.992 0.008
#> SRR821043 2 0.0000 0.9104 0.000 1.000
#> SRR1455653 2 0.0000 0.9104 0.000 1.000
#> SRR1335236 1 0.5946 0.8148 0.856 0.144
#> SRR1095383 2 0.0672 0.9066 0.008 0.992
#> SRR1479489 1 0.0376 0.9652 0.996 0.004
#> SRR1310433 2 0.9922 0.2330 0.448 0.552
#> SRR1073435 1 0.0376 0.9646 0.996 0.004
#> SRR659649 1 0.0376 0.9646 0.996 0.004
#> SRR1395999 1 0.0672 0.9653 0.992 0.008
#> SRR1105248 2 0.0376 0.9106 0.004 0.996
#> SRR1338257 1 0.0672 0.9653 0.992 0.008
#> SRR1499395 1 0.0376 0.9646 0.996 0.004
#> SRR1350002 1 0.9732 0.2754 0.596 0.404
#> SRR1489757 1 0.0376 0.9646 0.996 0.004
#> SRR1414637 1 0.0376 0.9646 0.996 0.004
#> SRR1478113 2 0.0376 0.9106 0.004 0.996
#> SRR1322477 1 0.0672 0.9653 0.992 0.008
#> SRR1478789 1 0.0376 0.9646 0.996 0.004
#> SRR1414185 1 0.3274 0.9157 0.940 0.060
#> SRR1069141 1 0.9850 0.1970 0.572 0.428
#> SRR1376852 1 0.0672 0.9653 0.992 0.008
#> SRR1323491 1 0.0672 0.9653 0.992 0.008
#> SRR1338103 1 0.0000 0.9648 1.000 0.000
#> SRR1472012 1 0.0376 0.9646 0.996 0.004
#> SRR1340325 1 0.0672 0.9653 0.992 0.008
#> SRR1087321 1 0.0376 0.9646 0.996 0.004
#> SRR1488790 1 0.0672 0.9653 0.992 0.008
#> SRR1334866 1 0.0376 0.9646 0.996 0.004
#> SRR1089446 1 0.0376 0.9646 0.996 0.004
#> SRR1344445 1 0.0376 0.9646 0.996 0.004
#> SRR1412969 1 0.3274 0.9157 0.940 0.060
#> SRR1071668 1 0.0376 0.9646 0.996 0.004
#> SRR1075804 1 0.0672 0.9653 0.992 0.008
#> SRR1383283 1 0.0376 0.9646 0.996 0.004
#> SRR1350239 2 0.0376 0.9106 0.004 0.996
#> SRR1353878 1 0.0672 0.9653 0.992 0.008
#> SRR1375721 1 0.0672 0.9653 0.992 0.008
#> SRR1083983 1 0.0376 0.9646 0.996 0.004
#> SRR1090095 1 0.0672 0.9653 0.992 0.008
#> SRR1414792 1 0.0672 0.9653 0.992 0.008
#> SRR1075102 2 0.0376 0.9106 0.004 0.996
#> SRR1098737 1 0.0672 0.9653 0.992 0.008
#> SRR1349409 1 0.0672 0.9653 0.992 0.008
#> SRR1413008 2 0.0376 0.9106 0.004 0.996
#> SRR1407179 1 0.0376 0.9646 0.996 0.004
#> SRR1095913 1 0.1184 0.9569 0.984 0.016
#> SRR1403544 1 0.0672 0.9653 0.992 0.008
#> SRR1490546 1 0.0672 0.9653 0.992 0.008
#> SRR807971 1 0.0376 0.9646 0.996 0.004
#> SRR1436228 1 0.0376 0.9646 0.996 0.004
#> SRR1445218 2 0.9922 0.2330 0.448 0.552
#> SRR1485438 1 0.0376 0.9646 0.996 0.004
#> SRR1358143 1 0.0376 0.9652 0.996 0.004
#> SRR1328760 1 0.0672 0.9653 0.992 0.008
#> SRR1380806 1 0.0672 0.9653 0.992 0.008
#> SRR1379426 1 0.0376 0.9646 0.996 0.004
#> SRR1087007 1 0.0376 0.9646 0.996 0.004
#> SRR1086256 1 0.0376 0.9646 0.996 0.004
#> SRR1346734 2 0.0376 0.9106 0.004 0.996
#> SRR1414515 1 0.0672 0.9653 0.992 0.008
#> SRR1082151 1 0.0672 0.9653 0.992 0.008
#> SRR1349320 2 0.0376 0.9106 0.004 0.996
#> SRR1317554 2 0.0000 0.9104 0.000 1.000
#> SRR1076022 1 0.7139 0.7389 0.804 0.196
#> SRR1339573 1 0.0376 0.9646 0.996 0.004
#> SRR1455878 1 0.0672 0.9653 0.992 0.008
#> SRR1446203 1 0.0376 0.9646 0.996 0.004
#> SRR1387397 1 0.0672 0.9653 0.992 0.008
#> SRR1402590 1 0.0672 0.9653 0.992 0.008
#> SRR1317532 1 0.0672 0.9653 0.992 0.008
#> SRR1331488 2 0.9580 0.4106 0.380 0.620
#> SRR1499675 1 0.0376 0.9646 0.996 0.004
#> SRR1440467 1 0.3274 0.9157 0.940 0.060
#> SRR807995 1 0.7139 0.7389 0.804 0.196
#> SRR1476485 2 0.0376 0.9106 0.004 0.996
#> SRR1388214 1 0.0672 0.9653 0.992 0.008
#> SRR1456051 1 0.0672 0.9653 0.992 0.008
#> SRR1473275 1 0.0376 0.9646 0.996 0.004
#> SRR1444083 1 0.0672 0.9653 0.992 0.008
#> SRR1313807 1 0.1184 0.9569 0.984 0.016
#> SRR1470751 1 0.0672 0.9653 0.992 0.008
#> SRR1403434 1 0.3274 0.9157 0.940 0.060
#> SRR1390540 1 0.0672 0.9653 0.992 0.008
#> SRR1093861 1 0.5946 0.8148 0.856 0.144
#> SRR1325290 1 0.0376 0.9646 0.996 0.004
#> SRR1070689 1 0.0672 0.9653 0.992 0.008
#> SRR1384049 1 0.0672 0.9653 0.992 0.008
#> SRR1081184 1 0.0672 0.9653 0.992 0.008
#> SRR1324295 1 0.0672 0.9653 0.992 0.008
#> SRR1365313 1 0.0376 0.9646 0.996 0.004
#> SRR1321877 1 0.0376 0.9646 0.996 0.004
#> SRR815711 1 0.0376 0.9646 0.996 0.004
#> SRR1433476 2 0.0672 0.9066 0.008 0.992
#> SRR1101883 1 0.0376 0.9646 0.996 0.004
#> SRR1433729 2 0.5946 0.7913 0.144 0.856
#> SRR1341877 1 0.0672 0.9653 0.992 0.008
#> SRR1090556 1 0.0672 0.9653 0.992 0.008
#> SRR1357389 1 0.0376 0.9646 0.996 0.004
#> SRR1404227 1 0.0376 0.9646 0.996 0.004
#> SRR1376830 1 0.0672 0.9653 0.992 0.008
#> SRR1500661 1 0.0672 0.9653 0.992 0.008
#> SRR1080294 2 0.0672 0.9066 0.008 0.992
#> SRR1336314 2 0.0376 0.9106 0.004 0.996
#> SRR1102152 1 0.0672 0.9653 0.992 0.008
#> SRR1345244 1 0.0376 0.9646 0.996 0.004
#> SRR1478637 1 0.0376 0.9646 0.996 0.004
#> SRR1443776 1 0.0376 0.9646 0.996 0.004
#> SRR1120939 1 0.0376 0.9646 0.996 0.004
#> SRR1080117 1 0.0376 0.9646 0.996 0.004
#> SRR1102899 1 0.9732 0.2754 0.596 0.404
#> SRR1091865 1 0.0672 0.9653 0.992 0.008
#> SRR1361072 1 0.0672 0.9653 0.992 0.008
#> SRR1487890 1 0.0672 0.9653 0.992 0.008
#> SRR1349456 1 0.0376 0.9646 0.996 0.004
#> SRR1389384 1 0.0672 0.9650 0.992 0.008
#> SRR1316096 1 0.9963 0.0632 0.536 0.464
#> SRR1408512 1 0.0672 0.9653 0.992 0.008
#> SRR1447547 2 0.0376 0.9106 0.004 0.996
#> SRR1354053 2 0.0000 0.9104 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1499040 3 0.3752 0.846 0.144 0.000 0.856
#> SRR1322312 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1324412 3 0.1860 0.931 0.052 0.000 0.948
#> SRR1100991 3 0.1964 0.928 0.056 0.000 0.944
#> SRR1349479 2 0.1031 0.941 0.000 0.976 0.024
#> SRR1431248 1 0.0424 0.955 0.992 0.000 0.008
#> SRR1405054 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1409790 3 0.1411 0.932 0.036 0.000 0.964
#> SRR1352507 3 0.4062 0.812 0.164 0.000 0.836
#> SRR1383763 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1468314 3 0.4504 0.765 0.000 0.196 0.804
#> SRR1473674 3 0.3941 0.807 0.000 0.156 0.844
#> SRR1390499 1 0.0000 0.963 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1335236 3 0.0661 0.916 0.004 0.008 0.988
#> SRR1095383 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1310433 3 0.4555 0.760 0.000 0.200 0.800
#> SRR1073435 3 0.1411 0.933 0.036 0.000 0.964
#> SRR659649 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1395999 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1105248 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1338257 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1499395 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1350002 3 0.4555 0.760 0.000 0.200 0.800
#> SRR1489757 3 0.1753 0.932 0.048 0.000 0.952
#> SRR1414637 3 0.0592 0.921 0.012 0.000 0.988
#> SRR1478113 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1478789 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1414185 3 0.1399 0.930 0.028 0.004 0.968
#> SRR1069141 3 0.4555 0.760 0.000 0.200 0.800
#> SRR1376852 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1338103 1 0.5859 0.461 0.656 0.000 0.344
#> SRR1472012 3 0.3879 0.836 0.152 0.000 0.848
#> SRR1340325 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1087321 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1488790 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1334866 3 0.1411 0.933 0.036 0.000 0.964
#> SRR1089446 3 0.1289 0.932 0.032 0.000 0.968
#> SRR1344445 3 0.1753 0.932 0.048 0.000 0.952
#> SRR1412969 3 0.1399 0.930 0.028 0.004 0.968
#> SRR1071668 3 0.1753 0.932 0.048 0.000 0.952
#> SRR1075804 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1383283 3 0.1289 0.932 0.032 0.000 0.968
#> SRR1350239 2 0.1031 0.941 0.000 0.976 0.024
#> SRR1353878 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1083983 1 0.6079 0.351 0.612 0.000 0.388
#> SRR1090095 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1413008 2 0.1031 0.941 0.000 0.976 0.024
#> SRR1407179 3 0.2165 0.923 0.064 0.000 0.936
#> SRR1095913 3 0.0592 0.921 0.012 0.000 0.988
#> SRR1403544 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.963 1.000 0.000 0.000
#> SRR807971 3 0.1753 0.932 0.048 0.000 0.952
#> SRR1436228 3 0.2165 0.924 0.064 0.000 0.936
#> SRR1445218 3 0.4555 0.760 0.000 0.200 0.800
#> SRR1485438 3 0.1031 0.922 0.024 0.000 0.976
#> SRR1358143 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1379426 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1087007 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1086256 3 0.0592 0.921 0.012 0.000 0.988
#> SRR1346734 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1082151 1 0.5835 0.511 0.660 0.000 0.340
#> SRR1349320 2 0.0424 0.948 0.000 0.992 0.008
#> SRR1317554 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1076022 3 0.1163 0.907 0.000 0.028 0.972
#> SRR1339573 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1455878 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1446203 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1387397 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1331488 2 0.6154 0.297 0.408 0.592 0.000
#> SRR1499675 3 0.1289 0.932 0.032 0.000 0.968
#> SRR1440467 3 0.1399 0.930 0.028 0.004 0.968
#> SRR807995 3 0.3038 0.855 0.000 0.104 0.896
#> SRR1476485 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1473275 3 0.2261 0.920 0.068 0.000 0.932
#> SRR1444083 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1313807 3 0.1031 0.928 0.024 0.000 0.976
#> SRR1470751 1 0.1289 0.926 0.968 0.000 0.032
#> SRR1403434 3 0.1399 0.930 0.028 0.004 0.968
#> SRR1390540 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1093861 3 0.0829 0.915 0.004 0.012 0.984
#> SRR1325290 3 0.2261 0.920 0.068 0.000 0.932
#> SRR1070689 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1365313 3 0.1753 0.933 0.048 0.000 0.952
#> SRR1321877 3 0.1643 0.934 0.044 0.000 0.956
#> SRR815711 3 0.1289 0.932 0.032 0.000 0.968
#> SRR1433476 3 0.5905 0.454 0.000 0.352 0.648
#> SRR1101883 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1433729 3 0.4931 0.747 0.000 0.232 0.768
#> SRR1341877 1 0.3192 0.834 0.888 0.000 0.112
#> SRR1090556 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1357389 3 0.1289 0.932 0.032 0.000 0.968
#> SRR1404227 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1376830 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1080294 3 0.6280 0.204 0.000 0.460 0.540
#> SRR1336314 2 0.0000 0.952 0.000 1.000 0.000
#> SRR1102152 1 0.0892 0.940 0.980 0.000 0.020
#> SRR1345244 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1478637 3 0.1860 0.931 0.052 0.000 0.948
#> SRR1443776 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1120939 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1080117 3 0.1643 0.934 0.044 0.000 0.956
#> SRR1102899 3 0.3340 0.840 0.000 0.120 0.880
#> SRR1091865 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1349456 3 0.1411 0.933 0.036 0.000 0.964
#> SRR1389384 1 0.5905 0.440 0.648 0.000 0.352
#> SRR1316096 3 0.4555 0.760 0.000 0.200 0.800
#> SRR1408512 1 0.0000 0.963 1.000 0.000 0.000
#> SRR1447547 2 0.5650 0.553 0.000 0.688 0.312
#> SRR1354053 2 0.0000 0.952 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.2814 0.87816 0.868 0.132 0.000 0.000
#> SRR1349562 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1353376 4 0.1211 0.88953 0.000 0.040 0.000 0.960
#> SRR1499040 3 0.7381 0.27766 0.180 0.328 0.492 0.000
#> SRR1322312 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1324412 3 0.1174 0.70325 0.020 0.012 0.968 0.000
#> SRR1100991 3 0.2197 0.68883 0.080 0.004 0.916 0.000
#> SRR1349479 4 0.6597 0.45235 0.000 0.088 0.372 0.540
#> SRR1431248 1 0.4904 0.80046 0.744 0.216 0.040 0.000
#> SRR1405054 1 0.0336 0.89813 0.992 0.008 0.000 0.000
#> SRR1312266 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1409790 3 0.1398 0.68782 0.004 0.040 0.956 0.000
#> SRR1352507 3 0.4671 0.57659 0.028 0.220 0.752 0.000
#> SRR1383763 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1468314 2 0.5694 0.75566 0.000 0.696 0.224 0.080
#> SRR1473674 2 0.5803 0.80416 0.000 0.700 0.196 0.104
#> SRR1390499 1 0.0592 0.89827 0.984 0.016 0.000 0.000
#> SRR821043 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1455653 4 0.0000 0.90045 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.4134 0.79168 0.000 0.740 0.260 0.000
#> SRR1095383 4 0.1389 0.88657 0.000 0.048 0.000 0.952
#> SRR1479489 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1310433 2 0.5850 0.79651 0.000 0.700 0.184 0.116
#> SRR1073435 3 0.3893 0.65279 0.008 0.196 0.796 0.000
#> SRR659649 3 0.2413 0.72110 0.020 0.064 0.916 0.000
#> SRR1395999 1 0.1302 0.89540 0.956 0.044 0.000 0.000
#> SRR1105248 4 0.1211 0.88953 0.000 0.040 0.000 0.960
#> SRR1338257 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1499395 3 0.2021 0.72144 0.012 0.056 0.932 0.000
#> SRR1350002 2 0.5963 0.79988 0.000 0.688 0.196 0.116
#> SRR1489757 3 0.0937 0.70597 0.012 0.012 0.976 0.000
#> SRR1414637 2 0.4220 0.79212 0.004 0.748 0.248 0.000
#> SRR1478113 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1322477 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1478789 3 0.5271 0.46227 0.020 0.340 0.640 0.000
#> SRR1414185 3 0.1902 0.66949 0.004 0.064 0.932 0.000
#> SRR1069141 2 0.5850 0.79651 0.000 0.700 0.184 0.116
#> SRR1376852 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1323491 1 0.2647 0.88118 0.880 0.120 0.000 0.000
#> SRR1338103 1 0.7093 0.21533 0.568 0.220 0.212 0.000
#> SRR1472012 3 0.7710 0.21589 0.256 0.296 0.448 0.000
#> SRR1340325 1 0.0336 0.89866 0.992 0.008 0.000 0.000
#> SRR1087321 3 0.2124 0.71893 0.008 0.068 0.924 0.000
#> SRR1488790 1 0.0188 0.89838 0.996 0.004 0.000 0.000
#> SRR1334866 3 0.4872 0.43924 0.004 0.356 0.640 0.000
#> SRR1089446 3 0.1661 0.67988 0.004 0.052 0.944 0.000
#> SRR1344445 3 0.2413 0.72156 0.020 0.064 0.916 0.000
#> SRR1412969 3 0.1743 0.68349 0.004 0.056 0.940 0.000
#> SRR1071668 3 0.2385 0.72029 0.028 0.052 0.920 0.000
#> SRR1075804 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1383283 3 0.4814 0.50357 0.008 0.316 0.676 0.000
#> SRR1350239 4 0.4231 0.80992 0.000 0.080 0.096 0.824
#> SRR1353878 1 0.2530 0.88577 0.888 0.112 0.000 0.000
#> SRR1375721 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1083983 1 0.7827 -0.25884 0.412 0.288 0.300 0.000
#> SRR1090095 1 0.2530 0.88238 0.888 0.112 0.000 0.000
#> SRR1414792 1 0.2530 0.88238 0.888 0.112 0.000 0.000
#> SRR1075102 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1098737 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1349409 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1413008 4 0.4231 0.80992 0.000 0.080 0.096 0.824
#> SRR1407179 3 0.6881 0.36296 0.120 0.340 0.540 0.000
#> SRR1095913 2 0.4103 0.79171 0.000 0.744 0.256 0.000
#> SRR1403544 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1490546 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR807971 3 0.2385 0.72029 0.028 0.052 0.920 0.000
#> SRR1436228 3 0.6688 0.32298 0.096 0.368 0.536 0.000
#> SRR1445218 2 0.5850 0.79651 0.000 0.700 0.184 0.116
#> SRR1485438 2 0.4328 0.79173 0.008 0.748 0.244 0.000
#> SRR1358143 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1328760 1 0.2814 0.88063 0.868 0.132 0.000 0.000
#> SRR1380806 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1379426 3 0.2522 0.71967 0.016 0.076 0.908 0.000
#> SRR1087007 3 0.2300 0.72123 0.016 0.064 0.920 0.000
#> SRR1086256 2 0.4103 0.79171 0.000 0.744 0.256 0.000
#> SRR1346734 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1414515 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1082151 2 0.5272 0.57714 0.136 0.752 0.112 0.000
#> SRR1349320 4 0.0469 0.89745 0.000 0.012 0.000 0.988
#> SRR1317554 4 0.0000 0.90045 0.000 0.000 0.000 1.000
#> SRR1076022 2 0.4283 0.79513 0.000 0.740 0.256 0.004
#> SRR1339573 3 0.2335 0.72176 0.020 0.060 0.920 0.000
#> SRR1455878 1 0.0336 0.89797 0.992 0.008 0.000 0.000
#> SRR1446203 3 0.4797 0.57522 0.020 0.260 0.720 0.000
#> SRR1387397 1 0.1211 0.89551 0.960 0.040 0.000 0.000
#> SRR1402590 1 0.0000 0.89830 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1331488 4 0.7867 0.00475 0.380 0.196 0.008 0.416
#> SRR1499675 3 0.0779 0.70075 0.004 0.016 0.980 0.000
#> SRR1440467 3 0.1743 0.68349 0.004 0.056 0.940 0.000
#> SRR807995 2 0.4485 0.80008 0.000 0.740 0.248 0.012
#> SRR1476485 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1388214 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1456051 1 0.0000 0.89830 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.6824 0.38756 0.120 0.324 0.556 0.000
#> SRR1444083 1 0.3498 0.86707 0.832 0.160 0.008 0.000
#> SRR1313807 3 0.3908 0.64088 0.004 0.212 0.784 0.000
#> SRR1470751 2 0.4453 0.41294 0.244 0.744 0.012 0.000
#> SRR1403434 3 0.1743 0.68349 0.004 0.056 0.940 0.000
#> SRR1390540 1 0.3257 0.87124 0.844 0.152 0.004 0.000
#> SRR1093861 2 0.4103 0.79171 0.000 0.744 0.256 0.000
#> SRR1325290 3 0.7290 0.31750 0.168 0.328 0.504 0.000
#> SRR1070689 1 0.0000 0.89830 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0336 0.89797 0.992 0.008 0.000 0.000
#> SRR1081184 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1324295 1 0.0000 0.89830 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.5632 0.44384 0.036 0.340 0.624 0.000
#> SRR1321877 3 0.5213 0.48181 0.020 0.328 0.652 0.000
#> SRR815711 3 0.1398 0.68668 0.004 0.040 0.956 0.000
#> SRR1433476 3 0.4869 0.49789 0.000 0.088 0.780 0.132
#> SRR1101883 3 0.2335 0.72151 0.020 0.060 0.920 0.000
#> SRR1433729 2 0.7450 0.27817 0.000 0.424 0.404 0.172
#> SRR1341877 1 0.5056 0.80535 0.760 0.164 0.076 0.000
#> SRR1090556 1 0.4793 0.81279 0.756 0.204 0.040 0.000
#> SRR1357389 3 0.1305 0.68899 0.004 0.036 0.960 0.000
#> SRR1404227 3 0.5271 0.46227 0.020 0.340 0.640 0.000
#> SRR1376830 1 0.0336 0.89797 0.992 0.008 0.000 0.000
#> SRR1500661 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1080294 3 0.7650 -0.14822 0.000 0.364 0.424 0.212
#> SRR1336314 4 0.0188 0.90049 0.000 0.004 0.000 0.996
#> SRR1102152 1 0.2408 0.88573 0.896 0.104 0.000 0.000
#> SRR1345244 3 0.2300 0.72123 0.016 0.064 0.920 0.000
#> SRR1478637 3 0.5823 0.42279 0.044 0.348 0.608 0.000
#> SRR1443776 3 0.5233 0.47593 0.020 0.332 0.648 0.000
#> SRR1120939 3 0.2924 0.70777 0.016 0.100 0.884 0.000
#> SRR1080117 3 0.2300 0.72123 0.016 0.064 0.920 0.000
#> SRR1102899 2 0.5067 0.80883 0.000 0.736 0.216 0.048
#> SRR1091865 1 0.2345 0.88820 0.900 0.100 0.000 0.000
#> SRR1361072 1 0.3401 0.86961 0.840 0.152 0.008 0.000
#> SRR1487890 1 0.0188 0.89772 0.996 0.004 0.000 0.000
#> SRR1349456 3 0.4990 0.44628 0.008 0.352 0.640 0.000
#> SRR1389384 1 0.7143 -0.12925 0.460 0.408 0.132 0.000
#> SRR1316096 2 0.5850 0.79651 0.000 0.700 0.184 0.116
#> SRR1408512 1 0.3300 0.87405 0.848 0.144 0.008 0.000
#> SRR1447547 3 0.7497 -0.05206 0.000 0.224 0.496 0.280
#> SRR1354053 4 0.0000 0.90045 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.2074 0.6748 0.896 0.000 0.000 0.000 0.104
#> SRR1349562 1 0.4278 0.7355 0.548 0.000 0.000 0.000 0.452
#> SRR1353376 4 0.2519 0.9079 0.000 0.016 0.000 0.884 0.100
#> SRR1499040 5 0.6235 0.6384 0.020 0.084 0.416 0.000 0.480
#> SRR1322312 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1324412 3 0.1074 0.6794 0.004 0.012 0.968 0.000 0.016
#> SRR1100991 3 0.0566 0.6821 0.004 0.000 0.984 0.000 0.012
#> SRR1349479 3 0.7552 0.2073 0.000 0.064 0.420 0.180 0.336
#> SRR1431248 1 0.3007 0.4599 0.864 0.004 0.104 0.000 0.028
#> SRR1405054 1 0.4302 0.7234 0.520 0.000 0.000 0.000 0.480
#> SRR1312266 1 0.0000 0.6459 1.000 0.000 0.000 0.000 0.000
#> SRR1409790 3 0.3609 0.6061 0.004 0.032 0.816 0.000 0.148
#> SRR1352507 3 0.1992 0.6601 0.032 0.000 0.924 0.000 0.044
#> SRR1383763 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1468314 2 0.2765 0.8227 0.000 0.896 0.036 0.024 0.044
#> SRR1473674 2 0.2082 0.8455 0.000 0.928 0.024 0.032 0.016
#> SRR1390499 1 0.3999 0.7345 0.656 0.000 0.000 0.000 0.344
#> SRR821043 4 0.0162 0.9445 0.000 0.000 0.000 0.996 0.004
#> SRR1455653 4 0.0162 0.9452 0.000 0.000 0.000 0.996 0.004
#> SRR1335236 2 0.1845 0.8422 0.000 0.928 0.056 0.000 0.016
#> SRR1095383 4 0.3134 0.8881 0.000 0.032 0.000 0.848 0.120
#> SRR1479489 1 0.4287 0.7247 0.540 0.000 0.000 0.000 0.460
#> SRR1310433 2 0.1854 0.8397 0.000 0.936 0.020 0.036 0.008
#> SRR1073435 3 0.4275 0.5708 0.020 0.104 0.800 0.000 0.076
#> SRR659649 3 0.0000 0.6819 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.4060 0.7118 0.640 0.000 0.000 0.000 0.360
#> SRR1105248 4 0.2573 0.9077 0.000 0.016 0.000 0.880 0.104
#> SRR1338257 1 0.0000 0.6459 1.000 0.000 0.000 0.000 0.000
#> SRR1499395 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1350002 2 0.2165 0.8448 0.000 0.924 0.024 0.036 0.016
#> SRR1489757 3 0.0968 0.6803 0.004 0.012 0.972 0.000 0.012
#> SRR1414637 2 0.5104 0.6611 0.004 0.700 0.100 0.000 0.196
#> SRR1478113 4 0.0671 0.9445 0.000 0.004 0.000 0.980 0.016
#> SRR1322477 1 0.0162 0.6432 0.996 0.000 0.000 0.000 0.004
#> SRR1478789 3 0.5258 0.2990 0.000 0.140 0.680 0.000 0.180
#> SRR1414185 3 0.4479 0.5276 0.000 0.036 0.700 0.000 0.264
#> SRR1069141 2 0.1728 0.8405 0.000 0.940 0.020 0.036 0.004
#> SRR1376852 1 0.4287 0.7247 0.540 0.000 0.000 0.000 0.460
#> SRR1323491 1 0.2648 0.6865 0.848 0.000 0.000 0.000 0.152
#> SRR1338103 5 0.6822 0.6291 0.120 0.044 0.320 0.000 0.516
#> SRR1472012 5 0.5762 0.6569 0.020 0.048 0.404 0.000 0.528
#> SRR1340325 1 0.4249 0.7377 0.568 0.000 0.000 0.000 0.432
#> SRR1087321 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1488790 1 0.4262 0.7384 0.560 0.000 0.000 0.000 0.440
#> SRR1334866 3 0.5392 0.2602 0.000 0.144 0.664 0.000 0.192
#> SRR1089446 3 0.4210 0.5600 0.000 0.036 0.740 0.000 0.224
#> SRR1344445 3 0.0162 0.6817 0.004 0.000 0.996 0.000 0.000
#> SRR1412969 3 0.4210 0.5600 0.000 0.036 0.740 0.000 0.224
#> SRR1071668 3 0.0451 0.6824 0.004 0.000 0.988 0.000 0.008
#> SRR1075804 1 0.0000 0.6459 1.000 0.000 0.000 0.000 0.000
#> SRR1383283 3 0.4805 0.4349 0.000 0.128 0.728 0.000 0.144
#> SRR1350239 4 0.4325 0.8188 0.000 0.048 0.004 0.756 0.192
#> SRR1353878 1 0.3039 0.7062 0.808 0.000 0.000 0.000 0.192
#> SRR1375721 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1083983 5 0.6219 0.6622 0.056 0.044 0.364 0.000 0.536
#> SRR1090095 1 0.2929 0.6925 0.820 0.000 0.000 0.000 0.180
#> SRR1414792 1 0.2966 0.6930 0.816 0.000 0.000 0.000 0.184
#> SRR1075102 4 0.0671 0.9445 0.000 0.004 0.000 0.980 0.016
#> SRR1098737 1 0.0162 0.6432 0.996 0.000 0.000 0.000 0.004
#> SRR1349409 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1413008 4 0.4325 0.8188 0.000 0.048 0.004 0.756 0.192
#> SRR1407179 5 0.6163 0.5905 0.020 0.076 0.448 0.000 0.456
#> SRR1095913 2 0.4936 0.6742 0.000 0.712 0.116 0.000 0.172
#> SRR1403544 1 0.4291 0.7321 0.536 0.000 0.000 0.000 0.464
#> SRR1490546 1 0.0162 0.6475 0.996 0.000 0.000 0.000 0.004
#> SRR807971 3 0.0451 0.6824 0.004 0.000 0.988 0.000 0.008
#> SRR1436228 3 0.6802 -0.5895 0.020 0.152 0.416 0.000 0.412
#> SRR1445218 2 0.1854 0.8397 0.000 0.936 0.020 0.036 0.008
#> SRR1485438 2 0.5187 0.6651 0.008 0.704 0.108 0.000 0.180
#> SRR1358143 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1328760 1 0.2929 0.7036 0.820 0.000 0.000 0.000 0.180
#> SRR1380806 1 0.4300 0.7250 0.524 0.000 0.000 0.000 0.476
#> SRR1379426 3 0.1043 0.6782 0.000 0.000 0.960 0.000 0.040
#> SRR1087007 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1086256 2 0.4797 0.6876 0.000 0.724 0.104 0.000 0.172
#> SRR1346734 4 0.0162 0.9445 0.000 0.000 0.000 0.996 0.004
#> SRR1414515 1 0.4302 0.7234 0.520 0.000 0.000 0.000 0.480
#> SRR1082151 2 0.6569 0.4675 0.264 0.556 0.024 0.000 0.156
#> SRR1349320 4 0.0912 0.9423 0.000 0.012 0.000 0.972 0.016
#> SRR1317554 4 0.0162 0.9452 0.000 0.000 0.000 0.996 0.004
#> SRR1076022 2 0.1740 0.8429 0.000 0.932 0.056 0.000 0.012
#> SRR1339573 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1455878 1 0.4283 0.7238 0.544 0.000 0.000 0.000 0.456
#> SRR1446203 3 0.3390 0.5491 0.000 0.060 0.840 0.000 0.100
#> SRR1387397 1 0.3796 0.7132 0.700 0.000 0.000 0.000 0.300
#> SRR1402590 1 0.4262 0.7384 0.560 0.000 0.000 0.000 0.440
#> SRR1317532 1 0.0290 0.6453 0.992 0.000 0.000 0.000 0.008
#> SRR1331488 1 0.4712 0.3169 0.736 0.004 0.000 0.180 0.080
#> SRR1499675 3 0.1364 0.6757 0.000 0.012 0.952 0.000 0.036
#> SRR1440467 3 0.4210 0.5600 0.000 0.036 0.740 0.000 0.224
#> SRR807995 2 0.1988 0.8452 0.000 0.928 0.048 0.008 0.016
#> SRR1476485 4 0.0290 0.9443 0.000 0.000 0.000 0.992 0.008
#> SRR1388214 1 0.0000 0.6459 1.000 0.000 0.000 0.000 0.000
#> SRR1456051 1 0.4249 0.7394 0.568 0.000 0.000 0.000 0.432
#> SRR1473275 3 0.5743 -0.5272 0.008 0.064 0.500 0.000 0.428
#> SRR1444083 1 0.0000 0.6459 1.000 0.000 0.000 0.000 0.000
#> SRR1313807 3 0.3749 0.5818 0.000 0.104 0.816 0.000 0.080
#> SRR1470751 2 0.5983 0.3567 0.380 0.504 0.000 0.000 0.116
#> SRR1403434 3 0.4210 0.5600 0.000 0.036 0.740 0.000 0.224
#> SRR1390540 1 0.0880 0.6522 0.968 0.000 0.000 0.000 0.032
#> SRR1093861 2 0.1845 0.8422 0.000 0.928 0.056 0.000 0.016
#> SRR1325290 5 0.6114 0.6208 0.020 0.072 0.436 0.000 0.472
#> SRR1070689 1 0.4262 0.7384 0.560 0.000 0.000 0.000 0.440
#> SRR1384049 1 0.4256 0.7399 0.564 0.000 0.000 0.000 0.436
#> SRR1081184 1 0.4294 0.7294 0.532 0.000 0.000 0.000 0.468
#> SRR1324295 1 0.4268 0.7379 0.556 0.000 0.000 0.000 0.444
#> SRR1365313 3 0.5444 0.2333 0.000 0.140 0.656 0.000 0.204
#> SRR1321877 3 0.4138 0.4613 0.000 0.064 0.776 0.000 0.160
#> SRR815711 3 0.4150 0.5643 0.000 0.036 0.748 0.000 0.216
#> SRR1433476 3 0.5654 0.4154 0.000 0.064 0.584 0.012 0.340
#> SRR1101883 3 0.0451 0.6824 0.004 0.000 0.988 0.000 0.008
#> SRR1433729 3 0.7639 0.0608 0.000 0.352 0.412 0.088 0.148
#> SRR1341877 1 0.4914 0.3271 0.712 0.000 0.180 0.000 0.108
#> SRR1090556 1 0.2787 0.5075 0.880 0.004 0.088 0.000 0.028
#> SRR1357389 3 0.3321 0.6144 0.000 0.032 0.832 0.000 0.136
#> SRR1404227 3 0.4444 0.4199 0.000 0.072 0.748 0.000 0.180
#> SRR1376830 1 0.4287 0.7247 0.540 0.000 0.000 0.000 0.460
#> SRR1500661 1 0.4287 0.7330 0.540 0.000 0.000 0.000 0.460
#> SRR1080294 3 0.7255 0.0702 0.000 0.372 0.428 0.052 0.148
#> SRR1336314 4 0.0162 0.9445 0.000 0.000 0.000 0.996 0.004
#> SRR1102152 1 0.3642 0.7049 0.760 0.008 0.000 0.000 0.232
#> SRR1345244 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1478637 3 0.6737 -0.4732 0.020 0.148 0.468 0.000 0.364
#> SRR1443776 3 0.4138 0.4613 0.000 0.064 0.776 0.000 0.160
#> SRR1120939 3 0.0162 0.6815 0.000 0.000 0.996 0.000 0.004
#> SRR1080117 3 0.0290 0.6807 0.000 0.000 0.992 0.000 0.008
#> SRR1102899 2 0.1756 0.8447 0.000 0.940 0.036 0.016 0.008
#> SRR1091865 1 0.3424 0.6998 0.760 0.000 0.000 0.000 0.240
#> SRR1361072 1 0.0794 0.6506 0.972 0.000 0.000 0.000 0.028
#> SRR1487890 1 0.4302 0.7234 0.520 0.000 0.000 0.000 0.480
#> SRR1349456 3 0.4698 0.3978 0.000 0.096 0.732 0.000 0.172
#> SRR1389384 5 0.8214 0.4603 0.216 0.184 0.188 0.000 0.412
#> SRR1316096 2 0.1854 0.8397 0.000 0.936 0.020 0.036 0.008
#> SRR1408512 1 0.1908 0.6814 0.908 0.000 0.000 0.000 0.092
#> SRR1447547 5 0.8256 -0.0951 0.320 0.060 0.260 0.020 0.340
#> SRR1354053 4 0.0162 0.9452 0.000 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.3944 0.53712 0.428 0.000 0.000 0.000 0.568 0.004
#> SRR1349562 1 0.0146 0.85618 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1353376 4 0.3893 0.83538 0.000 0.000 0.000 0.768 0.092 0.140
#> SRR1499040 6 0.6724 0.31207 0.120 0.032 0.384 0.000 0.032 0.432
#> SRR1322312 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0717 0.61781 0.000 0.000 0.976 0.000 0.008 0.016
#> SRR1100991 3 0.0520 0.61996 0.000 0.000 0.984 0.000 0.008 0.008
#> SRR1349479 6 0.6343 -0.01188 0.000 0.032 0.292 0.020 0.120 0.536
#> SRR1431248 5 0.4270 0.73358 0.164 0.000 0.036 0.000 0.756 0.044
#> SRR1405054 1 0.0146 0.85425 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1312266 5 0.3445 0.78248 0.260 0.000 0.000 0.000 0.732 0.008
#> SRR1409790 3 0.2848 0.50085 0.000 0.000 0.816 0.000 0.008 0.176
#> SRR1352507 3 0.1844 0.59371 0.004 0.000 0.924 0.000 0.048 0.024
#> SRR1383763 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468314 2 0.2420 0.71236 0.000 0.884 0.000 0.000 0.040 0.076
#> SRR1473674 2 0.1296 0.75268 0.000 0.948 0.004 0.004 0.000 0.044
#> SRR1390499 1 0.1387 0.79499 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR821043 4 0.0520 0.90097 0.000 0.000 0.000 0.984 0.008 0.008
#> SRR1455653 4 0.0622 0.90238 0.000 0.000 0.000 0.980 0.008 0.012
#> SRR1335236 2 0.1082 0.75312 0.000 0.956 0.004 0.000 0.000 0.040
#> SRR1095383 4 0.5633 0.72426 0.000 0.048 0.000 0.636 0.124 0.192
#> SRR1479489 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.1485 0.73951 0.000 0.944 0.000 0.004 0.024 0.028
#> SRR1073435 3 0.4711 0.46017 0.000 0.048 0.740 0.000 0.100 0.112
#> SRR659649 3 0.0508 0.62116 0.000 0.000 0.984 0.000 0.012 0.004
#> SRR1395999 1 0.3608 0.42265 0.716 0.000 0.000 0.000 0.272 0.012
#> SRR1105248 4 0.3985 0.83514 0.000 0.000 0.000 0.760 0.100 0.140
#> SRR1338257 5 0.3564 0.78109 0.264 0.000 0.000 0.000 0.724 0.012
#> SRR1499395 3 0.1556 0.60503 0.000 0.000 0.920 0.000 0.000 0.080
#> SRR1350002 2 0.1296 0.75268 0.000 0.948 0.004 0.004 0.000 0.044
#> SRR1489757 3 0.0622 0.61906 0.000 0.000 0.980 0.000 0.008 0.012
#> SRR1414637 2 0.6149 0.24791 0.004 0.480 0.088 0.000 0.048 0.380
#> SRR1478113 4 0.1152 0.89992 0.000 0.000 0.000 0.952 0.044 0.004
#> SRR1322477 5 0.3163 0.77942 0.232 0.000 0.000 0.000 0.764 0.004
#> SRR1478789 3 0.4355 0.11491 0.000 0.024 0.556 0.000 0.000 0.420
#> SRR1414185 3 0.5073 0.25277 0.000 0.004 0.548 0.000 0.072 0.376
#> SRR1069141 2 0.0653 0.74689 0.000 0.980 0.000 0.004 0.012 0.004
#> SRR1376852 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1323491 1 0.3996 -0.32256 0.512 0.000 0.000 0.000 0.484 0.004
#> SRR1338103 6 0.6924 0.38696 0.244 0.012 0.328 0.000 0.032 0.384
#> SRR1472012 6 0.6768 0.37342 0.176 0.016 0.348 0.000 0.032 0.428
#> SRR1340325 1 0.0146 0.85633 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1087321 3 0.1728 0.61085 0.000 0.004 0.924 0.000 0.008 0.064
#> SRR1488790 1 0.0547 0.84780 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1334866 3 0.5052 0.02809 0.004 0.028 0.512 0.000 0.020 0.436
#> SRR1089446 3 0.4902 0.27413 0.000 0.004 0.572 0.000 0.060 0.364
#> SRR1344445 3 0.0820 0.62085 0.000 0.000 0.972 0.000 0.012 0.016
#> SRR1412969 3 0.5009 0.26709 0.000 0.004 0.560 0.000 0.068 0.368
#> SRR1071668 3 0.0260 0.62114 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1075804 5 0.3244 0.77903 0.268 0.000 0.000 0.000 0.732 0.000
#> SRR1383283 3 0.5160 0.22788 0.000 0.048 0.604 0.000 0.032 0.316
#> SRR1350239 4 0.4596 0.75675 0.000 0.000 0.000 0.672 0.088 0.240
#> SRR1353878 1 0.4181 -0.32558 0.512 0.000 0.000 0.000 0.476 0.012
#> SRR1375721 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 6 0.6975 0.39633 0.244 0.016 0.308 0.000 0.032 0.400
#> SRR1090095 1 0.3659 0.14050 0.636 0.000 0.000 0.000 0.364 0.000
#> SRR1414792 1 0.3050 0.50692 0.764 0.000 0.000 0.000 0.236 0.000
#> SRR1075102 4 0.1219 0.89948 0.000 0.000 0.000 0.948 0.048 0.004
#> SRR1098737 5 0.3175 0.78305 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR1349409 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.4596 0.75675 0.000 0.000 0.000 0.672 0.088 0.240
#> SRR1407179 3 0.6022 -0.25436 0.104 0.016 0.444 0.000 0.012 0.424
#> SRR1095913 2 0.6282 0.19100 0.000 0.460 0.128 0.000 0.044 0.368
#> SRR1403544 1 0.0405 0.85453 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1490546 5 0.3288 0.77324 0.276 0.000 0.000 0.000 0.724 0.000
#> SRR807971 3 0.0622 0.61964 0.000 0.000 0.980 0.000 0.012 0.008
#> SRR1436228 6 0.7221 0.35187 0.116 0.052 0.340 0.000 0.056 0.436
#> SRR1445218 2 0.1485 0.73951 0.000 0.944 0.000 0.004 0.024 0.028
#> SRR1485438 2 0.6183 0.25071 0.004 0.492 0.108 0.000 0.040 0.356
#> SRR1358143 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1328760 5 0.4179 0.40209 0.472 0.000 0.000 0.000 0.516 0.012
#> SRR1380806 1 0.0000 0.85650 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.1889 0.59938 0.000 0.004 0.920 0.000 0.020 0.056
#> SRR1087007 3 0.1829 0.61103 0.000 0.004 0.920 0.000 0.012 0.064
#> SRR1086256 2 0.5967 0.26055 0.000 0.488 0.088 0.000 0.044 0.380
#> SRR1346734 4 0.0508 0.90045 0.000 0.000 0.000 0.984 0.012 0.004
#> SRR1414515 1 0.0405 0.85453 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1082151 5 0.6829 -0.02278 0.024 0.324 0.016 0.000 0.408 0.228
#> SRR1349320 4 0.1850 0.89814 0.000 0.008 0.000 0.924 0.052 0.016
#> SRR1317554 4 0.1176 0.89934 0.000 0.000 0.000 0.956 0.020 0.024
#> SRR1076022 2 0.0937 0.75387 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1339573 3 0.1444 0.60797 0.000 0.000 0.928 0.000 0.000 0.072
#> SRR1455878 1 0.1327 0.80220 0.936 0.000 0.000 0.000 0.064 0.000
#> SRR1446203 3 0.2848 0.51519 0.000 0.008 0.816 0.000 0.000 0.176
#> SRR1387397 1 0.4192 -0.05421 0.572 0.000 0.000 0.000 0.412 0.016
#> SRR1402590 1 0.0363 0.85362 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1317532 5 0.3244 0.77903 0.268 0.000 0.000 0.000 0.732 0.000
#> SRR1331488 5 0.3894 0.70334 0.152 0.000 0.000 0.064 0.776 0.008
#> SRR1499675 3 0.1367 0.60822 0.000 0.000 0.944 0.000 0.012 0.044
#> SRR1440467 3 0.4961 0.26987 0.000 0.004 0.564 0.000 0.064 0.368
#> SRR807995 2 0.1152 0.75220 0.000 0.952 0.004 0.000 0.000 0.044
#> SRR1476485 4 0.0717 0.90034 0.000 0.000 0.000 0.976 0.016 0.008
#> SRR1388214 5 0.3221 0.78090 0.264 0.000 0.000 0.000 0.736 0.000
#> SRR1456051 1 0.0363 0.85362 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1473275 3 0.5098 -0.03856 0.052 0.012 0.512 0.000 0.000 0.424
#> SRR1444083 5 0.3420 0.78045 0.240 0.000 0.000 0.000 0.748 0.012
#> SRR1313807 3 0.4664 0.47724 0.000 0.076 0.740 0.000 0.048 0.136
#> SRR1470751 5 0.6697 0.11301 0.040 0.304 0.004 0.000 0.444 0.208
#> SRR1403434 3 0.4961 0.26987 0.000 0.004 0.564 0.000 0.064 0.368
#> SRR1390540 5 0.3728 0.68949 0.344 0.000 0.000 0.000 0.652 0.004
#> SRR1093861 2 0.1219 0.75209 0.000 0.948 0.004 0.000 0.000 0.048
#> SRR1325290 6 0.6552 0.30616 0.132 0.016 0.392 0.000 0.032 0.428
#> SRR1070689 1 0.0260 0.85531 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1384049 1 0.1075 0.82929 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1081184 1 0.0146 0.85618 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324295 1 0.0458 0.85336 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1365313 3 0.4827 0.06590 0.008 0.024 0.536 0.000 0.008 0.424
#> SRR1321877 3 0.3945 0.22071 0.000 0.008 0.612 0.000 0.000 0.380
#> SRR815711 3 0.4700 0.29929 0.000 0.000 0.600 0.000 0.060 0.340
#> SRR1433476 6 0.5774 -0.04740 0.000 0.024 0.328 0.000 0.112 0.536
#> SRR1101883 3 0.0622 0.61964 0.000 0.000 0.980 0.000 0.012 0.008
#> SRR1433729 2 0.7902 0.21568 0.000 0.356 0.240 0.032 0.112 0.260
#> SRR1341877 5 0.6551 0.45956 0.188 0.004 0.200 0.000 0.540 0.068
#> SRR1090556 5 0.4268 0.73954 0.172 0.000 0.032 0.000 0.752 0.044
#> SRR1357389 3 0.2743 0.51177 0.000 0.000 0.828 0.000 0.008 0.164
#> SRR1404227 3 0.4205 0.13046 0.000 0.016 0.564 0.000 0.000 0.420
#> SRR1376830 1 0.1007 0.82394 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1500661 1 0.0363 0.84981 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1080294 2 0.7520 0.22613 0.000 0.372 0.244 0.008 0.112 0.264
#> SRR1336314 4 0.0405 0.90044 0.000 0.000 0.000 0.988 0.008 0.004
#> SRR1102152 1 0.4489 -0.05046 0.568 0.008 0.000 0.000 0.404 0.020
#> SRR1345244 3 0.1700 0.60275 0.000 0.004 0.916 0.000 0.000 0.080
#> SRR1478637 3 0.6178 -0.19528 0.048 0.040 0.448 0.000 0.032 0.432
#> SRR1443776 3 0.3741 0.32110 0.000 0.008 0.672 0.000 0.000 0.320
#> SRR1120939 3 0.1075 0.61272 0.000 0.000 0.952 0.000 0.000 0.048
#> SRR1080117 3 0.1728 0.61085 0.000 0.004 0.924 0.000 0.008 0.064
#> SRR1102899 2 0.1649 0.73900 0.000 0.932 0.000 0.000 0.036 0.032
#> SRR1091865 5 0.5561 0.32721 0.428 0.000 0.000 0.000 0.436 0.136
#> SRR1361072 5 0.3601 0.73319 0.312 0.000 0.000 0.000 0.684 0.004
#> SRR1487890 1 0.0146 0.85475 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1349456 3 0.4516 0.11920 0.000 0.020 0.552 0.000 0.008 0.420
#> SRR1389384 6 0.7954 0.30959 0.168 0.072 0.108 0.000 0.220 0.432
#> SRR1316096 2 0.1321 0.74182 0.000 0.952 0.000 0.004 0.020 0.024
#> SRR1408512 5 0.3564 0.76873 0.264 0.000 0.000 0.000 0.724 0.012
#> SRR1447547 6 0.5704 -0.00676 0.000 0.000 0.152 0.004 0.344 0.500
#> SRR1354053 4 0.0622 0.90238 0.000 0.000 0.000 0.980 0.008 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.779 0.926 0.960 0.4996 0.496 0.496
#> 3 3 0.814 0.870 0.943 0.3158 0.736 0.521
#> 4 4 0.792 0.809 0.893 0.1166 0.857 0.618
#> 5 5 0.764 0.651 0.786 0.0711 0.862 0.552
#> 6 6 0.795 0.735 0.863 0.0488 0.915 0.635
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0672 0.973 0.992 0.008
#> SRR1349562 1 0.0000 0.976 1.000 0.000
#> SRR1353376 2 0.0000 0.940 0.000 1.000
#> SRR1499040 1 0.0000 0.976 1.000 0.000
#> SRR1322312 1 0.0000 0.976 1.000 0.000
#> SRR1324412 2 0.9000 0.635 0.316 0.684
#> SRR1100991 1 0.0000 0.976 1.000 0.000
#> SRR1349479 2 0.0000 0.940 0.000 1.000
#> SRR1431248 1 0.0672 0.973 0.992 0.008
#> SRR1405054 1 0.0000 0.976 1.000 0.000
#> SRR1312266 1 0.0672 0.973 0.992 0.008
#> SRR1409790 2 0.7602 0.789 0.220 0.780
#> SRR1352507 2 0.6973 0.818 0.188 0.812
#> SRR1383763 1 0.0000 0.976 1.000 0.000
#> SRR1468314 2 0.0000 0.940 0.000 1.000
#> SRR1473674 2 0.0672 0.940 0.008 0.992
#> SRR1390499 1 0.0000 0.976 1.000 0.000
#> SRR821043 2 0.0000 0.940 0.000 1.000
#> SRR1455653 2 0.0000 0.940 0.000 1.000
#> SRR1335236 2 0.0672 0.940 0.008 0.992
#> SRR1095383 2 0.0000 0.940 0.000 1.000
#> SRR1479489 1 0.0000 0.976 1.000 0.000
#> SRR1310433 2 0.0000 0.940 0.000 1.000
#> SRR1073435 2 0.0000 0.940 0.000 1.000
#> SRR659649 2 0.6973 0.824 0.188 0.812
#> SRR1395999 1 0.0000 0.976 1.000 0.000
#> SRR1105248 2 0.0000 0.940 0.000 1.000
#> SRR1338257 1 0.0672 0.973 0.992 0.008
#> SRR1499395 2 0.6973 0.824 0.188 0.812
#> SRR1350002 2 0.0672 0.940 0.008 0.992
#> SRR1489757 2 0.7528 0.794 0.216 0.784
#> SRR1414637 1 0.9732 0.350 0.596 0.404
#> SRR1478113 2 0.0000 0.940 0.000 1.000
#> SRR1322477 1 0.0672 0.973 0.992 0.008
#> SRR1478789 2 0.7299 0.808 0.204 0.796
#> SRR1414185 2 0.0000 0.940 0.000 1.000
#> SRR1069141 2 0.0672 0.940 0.008 0.992
#> SRR1376852 1 0.0000 0.976 1.000 0.000
#> SRR1323491 1 0.0672 0.973 0.992 0.008
#> SRR1338103 1 0.0000 0.976 1.000 0.000
#> SRR1472012 1 0.0000 0.976 1.000 0.000
#> SRR1340325 1 0.0000 0.976 1.000 0.000
#> SRR1087321 2 0.0672 0.940 0.008 0.992
#> SRR1488790 1 0.0000 0.976 1.000 0.000
#> SRR1334866 2 0.0672 0.940 0.008 0.992
#> SRR1089446 2 0.0938 0.938 0.012 0.988
#> SRR1344445 2 0.7376 0.804 0.208 0.792
#> SRR1412969 2 0.0672 0.940 0.008 0.992
#> SRR1071668 1 0.5178 0.848 0.884 0.116
#> SRR1075804 1 0.0672 0.973 0.992 0.008
#> SRR1383283 2 0.0672 0.940 0.008 0.992
#> SRR1350239 2 0.0000 0.940 0.000 1.000
#> SRR1353878 1 0.0000 0.976 1.000 0.000
#> SRR1375721 1 0.0000 0.976 1.000 0.000
#> SRR1083983 1 0.0000 0.976 1.000 0.000
#> SRR1090095 1 0.0672 0.973 0.992 0.008
#> SRR1414792 1 0.0672 0.973 0.992 0.008
#> SRR1075102 2 0.0000 0.940 0.000 1.000
#> SRR1098737 1 0.0672 0.973 0.992 0.008
#> SRR1349409 1 0.0000 0.976 1.000 0.000
#> SRR1413008 2 0.0000 0.940 0.000 1.000
#> SRR1407179 1 0.0000 0.976 1.000 0.000
#> SRR1095913 2 0.0672 0.940 0.008 0.992
#> SRR1403544 1 0.0000 0.976 1.000 0.000
#> SRR1490546 1 0.0672 0.973 0.992 0.008
#> SRR807971 2 0.7376 0.804 0.208 0.792
#> SRR1436228 1 0.0000 0.976 1.000 0.000
#> SRR1445218 2 0.0000 0.940 0.000 1.000
#> SRR1485438 1 0.1414 0.961 0.980 0.020
#> SRR1358143 1 0.0000 0.976 1.000 0.000
#> SRR1328760 1 0.0000 0.976 1.000 0.000
#> SRR1380806 1 0.0000 0.976 1.000 0.000
#> SRR1379426 2 0.0000 0.940 0.000 1.000
#> SRR1087007 2 0.0672 0.940 0.008 0.992
#> SRR1086256 2 0.0672 0.940 0.008 0.992
#> SRR1346734 2 0.0000 0.940 0.000 1.000
#> SRR1414515 1 0.0000 0.976 1.000 0.000
#> SRR1082151 1 0.3114 0.926 0.944 0.056
#> SRR1349320 2 0.0000 0.940 0.000 1.000
#> SRR1317554 2 0.0000 0.940 0.000 1.000
#> SRR1076022 2 0.0672 0.940 0.008 0.992
#> SRR1339573 2 0.7376 0.804 0.208 0.792
#> SRR1455878 1 0.0000 0.976 1.000 0.000
#> SRR1446203 2 0.7139 0.816 0.196 0.804
#> SRR1387397 1 0.0000 0.976 1.000 0.000
#> SRR1402590 1 0.0000 0.976 1.000 0.000
#> SRR1317532 1 0.0672 0.973 0.992 0.008
#> SRR1331488 1 0.7139 0.759 0.804 0.196
#> SRR1499675 2 0.1843 0.931 0.028 0.972
#> SRR1440467 2 0.0672 0.940 0.008 0.992
#> SRR807995 2 0.0672 0.940 0.008 0.992
#> SRR1476485 2 0.0000 0.940 0.000 1.000
#> SRR1388214 1 0.0672 0.973 0.992 0.008
#> SRR1456051 1 0.0000 0.976 1.000 0.000
#> SRR1473275 1 0.0000 0.976 1.000 0.000
#> SRR1444083 1 0.0672 0.973 0.992 0.008
#> SRR1313807 2 0.0000 0.940 0.000 1.000
#> SRR1470751 1 0.6531 0.797 0.832 0.168
#> SRR1403434 2 0.0672 0.940 0.008 0.992
#> SRR1390540 1 0.0672 0.973 0.992 0.008
#> SRR1093861 2 0.0672 0.940 0.008 0.992
#> SRR1325290 1 0.0000 0.976 1.000 0.000
#> SRR1070689 1 0.0000 0.976 1.000 0.000
#> SRR1384049 1 0.0000 0.976 1.000 0.000
#> SRR1081184 1 0.0000 0.976 1.000 0.000
#> SRR1324295 1 0.0000 0.976 1.000 0.000
#> SRR1365313 1 0.9129 0.447 0.672 0.328
#> SRR1321877 2 0.7376 0.804 0.208 0.792
#> SRR815711 2 0.1633 0.933 0.024 0.976
#> SRR1433476 2 0.0000 0.940 0.000 1.000
#> SRR1101883 2 0.7219 0.812 0.200 0.800
#> SRR1433729 2 0.0000 0.940 0.000 1.000
#> SRR1341877 1 0.0672 0.973 0.992 0.008
#> SRR1090556 1 0.0672 0.973 0.992 0.008
#> SRR1357389 2 0.6973 0.824 0.188 0.812
#> SRR1404227 2 0.7376 0.804 0.208 0.792
#> SRR1376830 1 0.0000 0.976 1.000 0.000
#> SRR1500661 1 0.0000 0.976 1.000 0.000
#> SRR1080294 2 0.0000 0.940 0.000 1.000
#> SRR1336314 2 0.0000 0.940 0.000 1.000
#> SRR1102152 1 0.3274 0.921 0.940 0.060
#> SRR1345244 2 0.6973 0.824 0.188 0.812
#> SRR1478637 1 0.0000 0.976 1.000 0.000
#> SRR1443776 2 0.6973 0.824 0.188 0.812
#> SRR1120939 2 0.6343 0.845 0.160 0.840
#> SRR1080117 2 0.0672 0.940 0.008 0.992
#> SRR1102899 2 0.0672 0.940 0.008 0.992
#> SRR1091865 1 0.0000 0.976 1.000 0.000
#> SRR1361072 1 0.0672 0.973 0.992 0.008
#> SRR1487890 1 0.0000 0.976 1.000 0.000
#> SRR1349456 2 0.0672 0.940 0.008 0.992
#> SRR1389384 1 0.0000 0.976 1.000 0.000
#> SRR1316096 2 0.0000 0.940 0.000 1.000
#> SRR1408512 1 0.0000 0.976 1.000 0.000
#> SRR1447547 2 0.0000 0.940 0.000 1.000
#> SRR1354053 2 0.0000 0.940 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1499040 3 0.4346 0.746 0.184 0.000 0.816
#> SRR1322312 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1349479 2 0.0424 0.904 0.000 0.992 0.008
#> SRR1431248 1 0.0424 0.973 0.992 0.008 0.000
#> SRR1405054 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1352507 2 0.5497 0.607 0.000 0.708 0.292
#> SRR1383763 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1473674 2 0.6154 0.230 0.000 0.592 0.408
#> SRR1390499 1 0.0000 0.979 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1335236 3 0.4605 0.735 0.000 0.204 0.796
#> SRR1095383 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1310433 2 0.3267 0.810 0.000 0.884 0.116
#> SRR1073435 2 0.1163 0.892 0.000 0.972 0.028
#> SRR659649 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1105248 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1338257 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1350002 2 0.5859 0.415 0.000 0.656 0.344
#> SRR1489757 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1414637 3 0.5529 0.616 0.000 0.296 0.704
#> SRR1478113 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1322477 1 0.0237 0.976 0.996 0.004 0.000
#> SRR1478789 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1414185 2 0.4887 0.701 0.000 0.772 0.228
#> SRR1069141 2 0.6079 0.294 0.000 0.612 0.388
#> SRR1376852 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1338103 1 0.5431 0.581 0.716 0.000 0.284
#> SRR1472012 3 0.4399 0.742 0.188 0.000 0.812
#> SRR1340325 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1334866 3 0.0424 0.896 0.000 0.008 0.992
#> SRR1089446 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1412969 3 0.0892 0.887 0.000 0.020 0.980
#> SRR1071668 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1383283 3 0.4235 0.765 0.000 0.176 0.824
#> SRR1350239 2 0.0424 0.904 0.000 0.992 0.008
#> SRR1353878 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1083983 3 0.5988 0.468 0.368 0.000 0.632
#> SRR1090095 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1098737 1 0.2356 0.912 0.928 0.072 0.000
#> SRR1349409 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1413008 2 0.0424 0.904 0.000 0.992 0.008
#> SRR1407179 3 0.2356 0.851 0.072 0.000 0.928
#> SRR1095913 3 0.6026 0.466 0.000 0.376 0.624
#> SRR1403544 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.979 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1436228 3 0.4796 0.706 0.220 0.000 0.780
#> SRR1445218 2 0.0424 0.903 0.000 0.992 0.008
#> SRR1485438 3 0.6243 0.745 0.100 0.124 0.776
#> SRR1358143 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1379426 2 0.4974 0.691 0.000 0.764 0.236
#> SRR1087007 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1086256 3 0.5497 0.622 0.000 0.292 0.708
#> SRR1346734 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1082151 1 0.4099 0.818 0.852 0.140 0.008
#> SRR1349320 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1076022 3 0.6111 0.422 0.000 0.396 0.604
#> SRR1339573 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1331488 2 0.5178 0.616 0.256 0.744 0.000
#> SRR1499675 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1440467 3 0.0000 0.899 0.000 0.000 1.000
#> SRR807995 3 0.6126 0.412 0.000 0.400 0.600
#> SRR1476485 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1473275 3 0.0424 0.895 0.008 0.000 0.992
#> SRR1444083 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1313807 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1470751 1 0.4605 0.730 0.796 0.204 0.000
#> SRR1403434 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1093861 3 0.5497 0.622 0.000 0.292 0.708
#> SRR1325290 3 0.1860 0.867 0.052 0.000 0.948
#> SRR1070689 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1365313 3 0.0424 0.895 0.008 0.000 0.992
#> SRR1321877 3 0.0000 0.899 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1433476 2 0.4121 0.770 0.000 0.832 0.168
#> SRR1101883 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1341877 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1090556 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.906 0.000 1.000 0.000
#> SRR1102152 1 0.2878 0.880 0.904 0.096 0.000
#> SRR1345244 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1478637 3 0.1163 0.883 0.028 0.000 0.972
#> SRR1443776 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1102899 3 0.6126 0.412 0.000 0.400 0.600
#> SRR1091865 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.899 0.000 0.000 1.000
#> SRR1389384 1 0.4121 0.777 0.832 0.000 0.168
#> SRR1316096 2 0.2261 0.858 0.000 0.932 0.068
#> SRR1408512 1 0.0000 0.979 1.000 0.000 0.000
#> SRR1447547 2 0.0424 0.904 0.000 0.992 0.008
#> SRR1354053 2 0.0000 0.906 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1499040 2 0.4331 0.480 0.000 0.712 0.288 0.000
#> SRR1322312 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1349479 4 0.3569 0.790 0.000 0.000 0.196 0.804
#> SRR1431248 1 0.4164 0.635 0.736 0.000 0.000 0.264
#> SRR1405054 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1312266 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1409790 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1352507 4 0.4454 0.657 0.000 0.000 0.308 0.692
#> SRR1383763 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.3400 0.708 0.000 0.180 0.000 0.820
#> SRR1473674 2 0.3610 0.706 0.000 0.800 0.000 0.200
#> SRR1390499 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1095383 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1479489 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.3801 0.692 0.000 0.780 0.000 0.220
#> SRR1073435 4 0.4454 0.627 0.000 0.308 0.000 0.692
#> SRR659649 3 0.3400 0.843 0.000 0.180 0.820 0.000
#> SRR1395999 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1338257 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.2921 0.847 0.000 0.140 0.860 0.000
#> SRR1350002 2 0.3801 0.692 0.000 0.780 0.000 0.220
#> SRR1489757 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1414637 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1478113 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1322477 1 0.4277 0.606 0.720 0.000 0.000 0.280
#> SRR1478789 2 0.4382 0.467 0.000 0.704 0.296 0.000
#> SRR1414185 3 0.4605 0.286 0.000 0.000 0.664 0.336
#> SRR1069141 2 0.3801 0.692 0.000 0.780 0.000 0.220
#> SRR1376852 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1338103 2 0.5099 0.327 0.380 0.612 0.008 0.000
#> SRR1472012 2 0.4331 0.480 0.000 0.712 0.288 0.000
#> SRR1340325 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.3528 0.838 0.000 0.192 0.808 0.000
#> SRR1488790 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.4222 0.498 0.000 0.728 0.272 0.000
#> SRR1089446 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.3444 0.842 0.000 0.184 0.816 0.000
#> SRR1412969 3 0.0188 0.843 0.000 0.000 0.996 0.004
#> SRR1071668 3 0.0592 0.847 0.000 0.016 0.984 0.000
#> SRR1075804 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1383283 2 0.2081 0.712 0.000 0.916 0.000 0.084
#> SRR1350239 4 0.3528 0.793 0.000 0.000 0.192 0.808
#> SRR1353878 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1083983 2 0.5810 0.477 0.256 0.672 0.072 0.000
#> SRR1090095 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.4817 0.362 0.612 0.000 0.000 0.388
#> SRR1349409 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.3528 0.793 0.000 0.000 0.192 0.808
#> SRR1407179 2 0.4356 0.474 0.000 0.708 0.292 0.000
#> SRR1095913 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1403544 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR807971 3 0.3400 0.843 0.000 0.180 0.820 0.000
#> SRR1436228 2 0.0000 0.686 0.000 1.000 0.000 0.000
#> SRR1445218 2 0.3801 0.692 0.000 0.780 0.000 0.220
#> SRR1485438 2 0.0336 0.690 0.000 0.992 0.000 0.008
#> SRR1358143 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.6806 0.353 0.000 0.112 0.544 0.344
#> SRR1087007 3 0.3528 0.838 0.000 0.192 0.808 0.000
#> SRR1086256 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1346734 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.3852 0.708 0.012 0.808 0.000 0.180
#> SRR1349320 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1076022 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1339573 3 0.3528 0.838 0.000 0.192 0.808 0.000
#> SRR1455878 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.3764 0.817 0.000 0.216 0.784 0.000
#> SRR1387397 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1331488 4 0.3569 0.702 0.196 0.000 0.000 0.804
#> SRR1499675 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1440467 3 0.0188 0.843 0.000 0.000 0.996 0.004
#> SRR807995 2 0.3569 0.708 0.000 0.804 0.000 0.196
#> SRR1476485 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1456051 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1473275 2 0.4866 0.184 0.000 0.596 0.404 0.000
#> SRR1444083 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1313807 4 0.0592 0.890 0.000 0.016 0.000 0.984
#> SRR1470751 2 0.4059 0.700 0.012 0.788 0.000 0.200
#> SRR1403434 3 0.0188 0.843 0.000 0.000 0.996 0.004
#> SRR1390540 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.3528 0.710 0.000 0.808 0.000 0.192
#> SRR1325290 2 0.4331 0.480 0.000 0.712 0.288 0.000
#> SRR1070689 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.4356 0.474 0.000 0.708 0.292 0.000
#> SRR1321877 3 0.3837 0.809 0.000 0.224 0.776 0.000
#> SRR815711 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1433476 4 0.3764 0.770 0.000 0.000 0.216 0.784
#> SRR1101883 3 0.3400 0.843 0.000 0.180 0.820 0.000
#> SRR1433729 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1341877 1 0.3356 0.766 0.824 0.176 0.000 0.000
#> SRR1090556 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1357389 3 0.0000 0.845 0.000 0.000 1.000 0.000
#> SRR1404227 2 0.4477 0.435 0.000 0.688 0.312 0.000
#> SRR1376830 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1336314 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.4635 0.740 0.796 0.124 0.000 0.080
#> SRR1345244 3 0.3528 0.838 0.000 0.192 0.808 0.000
#> SRR1478637 2 0.4277 0.489 0.000 0.720 0.280 0.000
#> SRR1443776 3 0.3837 0.809 0.000 0.224 0.776 0.000
#> SRR1120939 3 0.3569 0.835 0.000 0.196 0.804 0.000
#> SRR1080117 3 0.3528 0.838 0.000 0.192 0.808 0.000
#> SRR1102899 2 0.3764 0.695 0.000 0.784 0.000 0.216
#> SRR1091865 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1361072 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1349456 2 0.4382 0.467 0.000 0.704 0.296 0.000
#> SRR1389384 2 0.2469 0.652 0.108 0.892 0.000 0.000
#> SRR1316096 2 0.3801 0.692 0.000 0.780 0.000 0.220
#> SRR1408512 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1447547 4 0.3569 0.790 0.000 0.000 0.196 0.804
#> SRR1354053 4 0.0000 0.902 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 5 0.4262 0.8868 0.440 0.000 0.000 0.000 0.560
#> SRR1349562 1 0.0609 0.7000 0.980 0.000 0.000 0.000 0.020
#> SRR1353376 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1499040 3 0.8431 -0.0574 0.304 0.196 0.316 0.000 0.184
#> SRR1322312 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.4960 0.7427 0.000 0.000 0.688 0.080 0.232
#> SRR1100991 3 0.4960 0.7427 0.000 0.000 0.688 0.080 0.232
#> SRR1349479 4 0.2127 0.7649 0.000 0.000 0.000 0.892 0.108
#> SRR1431248 5 0.4517 0.8371 0.372 0.008 0.000 0.004 0.616
#> SRR1405054 1 0.0162 0.7087 0.996 0.000 0.000 0.000 0.004
#> SRR1312266 5 0.4249 0.8926 0.432 0.000 0.000 0.000 0.568
#> SRR1409790 3 0.4960 0.7427 0.000 0.000 0.688 0.080 0.232
#> SRR1352507 4 0.6246 0.2751 0.000 0.000 0.232 0.544 0.224
#> SRR1383763 1 0.0162 0.7087 0.996 0.000 0.000 0.000 0.004
#> SRR1468314 4 0.4262 0.4077 0.000 0.440 0.000 0.560 0.000
#> SRR1473674 2 0.0290 0.8756 0.000 0.992 0.000 0.008 0.000
#> SRR1390499 1 0.2561 0.5000 0.856 0.000 0.000 0.000 0.144
#> SRR821043 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1455653 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1335236 2 0.0000 0.8781 0.000 1.000 0.000 0.000 0.000
#> SRR1095383 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1479489 1 0.0162 0.7089 0.996 0.000 0.000 0.000 0.004
#> SRR1310433 2 0.0404 0.8735 0.000 0.988 0.000 0.012 0.000
#> SRR1073435 4 0.5337 0.4967 0.000 0.344 0.056 0.596 0.004
#> SRR659649 3 0.2707 0.7764 0.000 0.000 0.860 0.008 0.132
#> SRR1395999 1 0.1043 0.6863 0.960 0.000 0.000 0.000 0.040
#> SRR1105248 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1338257 5 0.4249 0.8926 0.432 0.000 0.000 0.000 0.568
#> SRR1499395 3 0.2408 0.7761 0.000 0.000 0.892 0.016 0.092
#> SRR1350002 2 0.0290 0.8756 0.000 0.992 0.000 0.008 0.000
#> SRR1489757 3 0.4960 0.7427 0.000 0.000 0.688 0.080 0.232
#> SRR1414637 2 0.0609 0.8739 0.000 0.980 0.000 0.000 0.020
#> SRR1478113 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1322477 5 0.4707 0.8618 0.392 0.000 0.000 0.020 0.588
#> SRR1478789 3 0.5502 0.4204 0.000 0.192 0.652 0.000 0.156
#> SRR1414185 4 0.6051 -0.1027 0.000 0.000 0.404 0.476 0.120
#> SRR1069141 2 0.0290 0.8756 0.000 0.992 0.000 0.008 0.000
#> SRR1376852 1 0.0510 0.7017 0.984 0.000 0.000 0.000 0.016
#> SRR1323491 1 0.4305 -0.7697 0.512 0.000 0.000 0.000 0.488
#> SRR1338103 1 0.6464 0.2545 0.524 0.008 0.296 0.000 0.172
#> SRR1472012 1 0.6599 0.2553 0.520 0.012 0.284 0.000 0.184
#> SRR1340325 1 0.0880 0.6909 0.968 0.000 0.000 0.000 0.032
#> SRR1087321 3 0.0000 0.7571 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.1410 0.6595 0.940 0.000 0.000 0.000 0.060
#> SRR1334866 2 0.6296 0.3286 0.000 0.480 0.360 0.000 0.160
#> SRR1089446 3 0.5032 0.7413 0.000 0.000 0.688 0.092 0.220
#> SRR1344445 3 0.2707 0.7764 0.000 0.000 0.860 0.008 0.132
#> SRR1412969 3 0.5073 0.7384 0.000 0.000 0.688 0.100 0.212
#> SRR1071668 3 0.4823 0.7466 0.000 0.000 0.700 0.072 0.228
#> SRR1075804 5 0.4249 0.8926 0.432 0.000 0.000 0.000 0.568
#> SRR1383283 2 0.2193 0.8385 0.000 0.912 0.060 0.000 0.028
#> SRR1350239 4 0.0162 0.8184 0.000 0.000 0.000 0.996 0.004
#> SRR1353878 1 0.3274 0.3279 0.780 0.000 0.000 0.000 0.220
#> SRR1375721 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.6400 0.3239 0.564 0.012 0.240 0.000 0.184
#> SRR1090095 1 0.4305 -0.7697 0.512 0.000 0.000 0.000 0.488
#> SRR1414792 1 0.4304 -0.7613 0.516 0.000 0.000 0.000 0.484
#> SRR1075102 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1098737 5 0.4590 0.8851 0.420 0.000 0.000 0.012 0.568
#> SRR1349409 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.0162 0.8184 0.000 0.000 0.000 0.996 0.004
#> SRR1407179 1 0.7979 -0.0395 0.364 0.108 0.348 0.000 0.180
#> SRR1095913 2 0.0000 0.8781 0.000 1.000 0.000 0.000 0.000
#> SRR1403544 1 0.0290 0.7074 0.992 0.000 0.000 0.000 0.008
#> SRR1490546 5 0.4249 0.8926 0.432 0.000 0.000 0.000 0.568
#> SRR807971 3 0.2629 0.7762 0.000 0.000 0.860 0.004 0.136
#> SRR1436228 2 0.6915 0.4869 0.036 0.536 0.228 0.000 0.200
#> SRR1445218 2 0.0290 0.8756 0.000 0.992 0.000 0.008 0.000
#> SRR1485438 2 0.3442 0.7908 0.000 0.836 0.060 0.000 0.104
#> SRR1358143 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.4150 -0.4217 0.612 0.000 0.000 0.000 0.388
#> SRR1380806 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 4 0.4743 0.0826 0.000 0.000 0.472 0.512 0.016
#> SRR1087007 3 0.0000 0.7571 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 2 0.0290 0.8768 0.000 0.992 0.000 0.000 0.008
#> SRR1346734 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1414515 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.1608 0.8461 0.000 0.928 0.000 0.000 0.072
#> SRR1349320 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1317554 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1076022 2 0.0000 0.8781 0.000 1.000 0.000 0.000 0.000
#> SRR1339573 3 0.0000 0.7571 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 1 0.0404 0.7044 0.988 0.000 0.000 0.000 0.012
#> SRR1446203 3 0.1281 0.7395 0.000 0.012 0.956 0.000 0.032
#> SRR1387397 1 0.1410 0.6863 0.940 0.000 0.000 0.000 0.060
#> SRR1402590 1 0.1121 0.6787 0.956 0.000 0.000 0.000 0.044
#> SRR1317532 5 0.4262 0.8865 0.440 0.000 0.000 0.000 0.560
#> SRR1331488 5 0.5393 0.0189 0.056 0.000 0.000 0.440 0.504
#> SRR1499675 3 0.5115 0.7418 0.000 0.000 0.676 0.092 0.232
#> SRR1440467 3 0.5032 0.7413 0.000 0.000 0.688 0.092 0.220
#> SRR807995 2 0.0000 0.8781 0.000 1.000 0.000 0.000 0.000
#> SRR1476485 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1388214 5 0.4249 0.8926 0.432 0.000 0.000 0.000 0.568
#> SRR1456051 1 0.1270 0.6698 0.948 0.000 0.000 0.000 0.052
#> SRR1473275 3 0.5064 0.5793 0.068 0.032 0.736 0.000 0.164
#> SRR1444083 5 0.4210 0.8815 0.412 0.000 0.000 0.000 0.588
#> SRR1313807 4 0.2773 0.8074 0.000 0.164 0.000 0.836 0.000
#> SRR1470751 2 0.1732 0.8405 0.000 0.920 0.000 0.000 0.080
#> SRR1403434 3 0.5032 0.7413 0.000 0.000 0.688 0.092 0.220
#> SRR1390540 5 0.4306 0.7967 0.492 0.000 0.000 0.000 0.508
#> SRR1093861 2 0.0000 0.8781 0.000 1.000 0.000 0.000 0.000
#> SRR1325290 1 0.7650 0.1216 0.436 0.076 0.304 0.000 0.184
#> SRR1070689 1 0.0794 0.6941 0.972 0.000 0.000 0.000 0.028
#> SRR1384049 1 0.0404 0.7053 0.988 0.000 0.000 0.000 0.012
#> SRR1081184 1 0.0162 0.7089 0.996 0.000 0.000 0.000 0.004
#> SRR1324295 1 0.1197 0.6744 0.952 0.000 0.000 0.000 0.048
#> SRR1365313 3 0.6057 0.3351 0.008 0.224 0.604 0.000 0.164
#> SRR1321877 3 0.3183 0.6535 0.000 0.016 0.828 0.000 0.156
#> SRR815711 3 0.5032 0.7413 0.000 0.000 0.688 0.092 0.220
#> SRR1433476 4 0.2674 0.7450 0.000 0.000 0.012 0.868 0.120
#> SRR1101883 3 0.2753 0.7759 0.000 0.000 0.856 0.008 0.136
#> SRR1433729 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1341877 1 0.6100 -0.4772 0.448 0.000 0.124 0.000 0.428
#> SRR1090556 5 0.4138 0.8516 0.384 0.000 0.000 0.000 0.616
#> SRR1357389 3 0.4960 0.7427 0.000 0.000 0.688 0.080 0.232
#> SRR1404227 3 0.4734 0.5487 0.000 0.108 0.732 0.000 0.160
#> SRR1376830 1 0.0162 0.7089 0.996 0.000 0.000 0.000 0.004
#> SRR1500661 1 0.0162 0.7089 0.996 0.000 0.000 0.000 0.004
#> SRR1080294 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1336314 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
#> SRR1102152 1 0.5128 0.3365 0.656 0.268 0.000 0.000 0.076
#> SRR1345244 3 0.0000 0.7571 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 2 0.7046 0.3850 0.032 0.484 0.300 0.000 0.184
#> SRR1443776 3 0.3098 0.6596 0.000 0.016 0.836 0.000 0.148
#> SRR1120939 3 0.1638 0.7690 0.000 0.004 0.932 0.000 0.064
#> SRR1080117 3 0.0000 0.7571 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.0290 0.8762 0.000 0.992 0.000 0.008 0.000
#> SRR1091865 1 0.2605 0.5799 0.852 0.000 0.000 0.000 0.148
#> SRR1361072 5 0.4287 0.8593 0.460 0.000 0.000 0.000 0.540
#> SRR1487890 1 0.0000 0.7098 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.5562 0.4058 0.000 0.200 0.644 0.000 0.156
#> SRR1389384 2 0.7231 0.5400 0.132 0.560 0.128 0.000 0.180
#> SRR1316096 2 0.0290 0.8756 0.000 0.992 0.000 0.008 0.000
#> SRR1408512 5 0.4273 0.8696 0.448 0.000 0.000 0.000 0.552
#> SRR1447547 4 0.2074 0.7678 0.000 0.000 0.000 0.896 0.104
#> SRR1354053 4 0.1908 0.8615 0.000 0.092 0.000 0.908 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.2854 0.8705 0.208 0.000 0.000 0.000 0.792 0.000
#> SRR1349562 1 0.0260 0.8825 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1353376 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1499040 6 0.2095 0.7064 0.028 0.016 0.000 0.000 0.040 0.916
#> SRR1322312 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324412 3 0.0260 0.7864 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1100991 3 0.0862 0.7850 0.004 0.000 0.972 0.000 0.008 0.016
#> SRR1349479 4 0.4033 0.7148 0.000 0.000 0.168 0.756 0.072 0.004
#> SRR1431248 5 0.2222 0.8505 0.084 0.008 0.000 0.000 0.896 0.012
#> SRR1405054 1 0.0632 0.8691 0.976 0.000 0.024 0.000 0.000 0.000
#> SRR1312266 5 0.2300 0.8913 0.144 0.000 0.000 0.000 0.856 0.000
#> SRR1409790 3 0.0405 0.7856 0.000 0.000 0.988 0.000 0.008 0.004
#> SRR1352507 3 0.3444 0.6961 0.000 0.000 0.812 0.140 0.012 0.036
#> SRR1383763 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1468314 4 0.3789 0.3503 0.000 0.416 0.000 0.584 0.000 0.000
#> SRR1473674 2 0.0260 0.9230 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1390499 1 0.0935 0.8682 0.964 0.000 0.000 0.000 0.032 0.004
#> SRR821043 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1455653 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1335236 2 0.0291 0.9228 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1095383 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1479489 1 0.0717 0.8730 0.976 0.000 0.000 0.000 0.016 0.008
#> SRR1310433 2 0.0458 0.9211 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1073435 4 0.5721 0.4082 0.000 0.332 0.036 0.560 0.008 0.064
#> SRR659649 3 0.3572 0.6519 0.000 0.000 0.764 0.000 0.032 0.204
#> SRR1395999 1 0.2302 0.7870 0.872 0.000 0.000 0.000 0.120 0.008
#> SRR1105248 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1338257 5 0.2442 0.8911 0.144 0.000 0.000 0.000 0.852 0.004
#> SRR1499395 3 0.5097 0.1876 0.000 0.000 0.508 0.004 0.068 0.420
#> SRR1350002 2 0.0405 0.9228 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1489757 3 0.0508 0.7860 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1414637 2 0.2362 0.8401 0.000 0.860 0.000 0.004 0.000 0.136
#> SRR1478113 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1322477 5 0.2538 0.8803 0.124 0.000 0.000 0.016 0.860 0.000
#> SRR1478789 6 0.0881 0.7099 0.000 0.008 0.008 0.000 0.012 0.972
#> SRR1414185 3 0.5804 0.5040 0.000 0.000 0.580 0.280 0.088 0.052
#> SRR1069141 2 0.0260 0.9230 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1376852 1 0.0603 0.8759 0.980 0.000 0.000 0.000 0.016 0.004
#> SRR1323491 1 0.3828 -0.0705 0.560 0.000 0.000 0.000 0.440 0.000
#> SRR1338103 6 0.4378 0.4968 0.280 0.012 0.000 0.000 0.032 0.676
#> SRR1472012 6 0.2881 0.6758 0.084 0.012 0.000 0.000 0.040 0.864
#> SRR1340325 1 0.0603 0.8793 0.980 0.000 0.000 0.000 0.016 0.004
#> SRR1087321 6 0.4735 0.0746 0.000 0.000 0.432 0.000 0.048 0.520
#> SRR1488790 1 0.0363 0.8802 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1334866 6 0.1225 0.7103 0.000 0.036 0.000 0.000 0.012 0.952
#> SRR1089446 3 0.1732 0.7743 0.000 0.000 0.920 0.004 0.072 0.004
#> SRR1344445 3 0.2805 0.7026 0.000 0.000 0.828 0.000 0.012 0.160
#> SRR1412969 3 0.4284 0.7034 0.000 0.000 0.768 0.028 0.092 0.112
#> SRR1071668 3 0.0891 0.7845 0.000 0.000 0.968 0.000 0.008 0.024
#> SRR1075804 5 0.2631 0.8878 0.180 0.000 0.000 0.000 0.820 0.000
#> SRR1383283 2 0.3829 0.6581 0.000 0.720 0.008 0.004 0.008 0.260
#> SRR1350239 4 0.0458 0.9093 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1353878 1 0.3337 0.5626 0.736 0.000 0.000 0.000 0.260 0.004
#> SRR1375721 1 0.0000 0.8824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 6 0.4484 0.5161 0.252 0.012 0.000 0.000 0.048 0.688
#> SRR1090095 1 0.3828 -0.0703 0.560 0.000 0.000 0.000 0.440 0.000
#> SRR1414792 1 0.3620 0.2835 0.648 0.000 0.000 0.000 0.352 0.000
#> SRR1075102 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1098737 5 0.2932 0.8885 0.164 0.000 0.000 0.016 0.820 0.000
#> SRR1349409 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1413008 4 0.0458 0.9093 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1407179 6 0.2024 0.7081 0.028 0.016 0.000 0.000 0.036 0.920
#> SRR1095913 2 0.0405 0.9220 0.000 0.988 0.000 0.004 0.000 0.008
#> SRR1403544 1 0.0000 0.8824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 5 0.2562 0.8903 0.172 0.000 0.000 0.000 0.828 0.000
#> SRR807971 3 0.2730 0.7110 0.000 0.000 0.836 0.000 0.012 0.152
#> SRR1436228 6 0.4400 0.4787 0.012 0.228 0.000 0.000 0.052 0.708
#> SRR1445218 2 0.0363 0.9225 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1485438 2 0.2212 0.8480 0.000 0.880 0.000 0.000 0.008 0.112
#> SRR1358143 1 0.0291 0.8817 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1328760 5 0.3993 0.2660 0.476 0.000 0.000 0.000 0.520 0.004
#> SRR1380806 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1379426 3 0.7009 0.3475 0.000 0.000 0.392 0.348 0.096 0.164
#> SRR1087007 6 0.4676 0.0984 0.000 0.000 0.428 0.000 0.044 0.528
#> SRR1086256 2 0.1806 0.8787 0.000 0.908 0.000 0.004 0.000 0.088
#> SRR1346734 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1414515 1 0.0000 0.8824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.1398 0.8977 0.000 0.940 0.000 0.000 0.052 0.008
#> SRR1349320 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1317554 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1076022 2 0.0146 0.9230 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1339573 6 0.4460 0.0739 0.000 0.000 0.452 0.000 0.028 0.520
#> SRR1455878 1 0.0632 0.8762 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1446203 6 0.4434 0.1526 0.000 0.000 0.428 0.000 0.028 0.544
#> SRR1387397 1 0.1082 0.8671 0.956 0.000 0.000 0.000 0.040 0.004
#> SRR1402590 1 0.0363 0.8802 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1317532 5 0.2730 0.8826 0.192 0.000 0.000 0.000 0.808 0.000
#> SRR1331488 5 0.3298 0.6273 0.008 0.000 0.000 0.236 0.756 0.000
#> SRR1499675 3 0.4338 0.5666 0.000 0.000 0.716 0.004 0.072 0.208
#> SRR1440467 3 0.2579 0.7673 0.000 0.000 0.876 0.004 0.088 0.032
#> SRR807995 2 0.0405 0.9234 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1476485 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1388214 5 0.2558 0.8925 0.156 0.000 0.000 0.000 0.840 0.004
#> SRR1456051 1 0.0858 0.8736 0.968 0.000 0.000 0.000 0.028 0.004
#> SRR1473275 6 0.1446 0.7137 0.012 0.012 0.012 0.000 0.012 0.952
#> SRR1444083 5 0.2442 0.8911 0.144 0.000 0.000 0.000 0.852 0.004
#> SRR1313807 4 0.1765 0.8545 0.000 0.096 0.000 0.904 0.000 0.000
#> SRR1470751 2 0.2431 0.8319 0.000 0.860 0.000 0.000 0.132 0.008
#> SRR1403434 3 0.2579 0.7673 0.000 0.000 0.876 0.004 0.088 0.032
#> SRR1390540 5 0.3706 0.5967 0.380 0.000 0.000 0.000 0.620 0.000
#> SRR1093861 2 0.0291 0.9228 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1325290 6 0.2422 0.6965 0.052 0.012 0.000 0.000 0.040 0.896
#> SRR1070689 1 0.0260 0.8813 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1384049 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1081184 1 0.0146 0.8828 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324295 1 0.0363 0.8802 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1365313 6 0.0976 0.7128 0.000 0.016 0.008 0.000 0.008 0.968
#> SRR1321877 6 0.1391 0.6920 0.000 0.000 0.040 0.000 0.016 0.944
#> SRR815711 3 0.1732 0.7743 0.000 0.000 0.920 0.004 0.072 0.004
#> SRR1433476 4 0.4144 0.6781 0.000 0.000 0.200 0.728 0.072 0.000
#> SRR1101883 3 0.2783 0.7116 0.000 0.000 0.836 0.000 0.016 0.148
#> SRR1433729 4 0.0260 0.9190 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1341877 1 0.5125 0.0865 0.540 0.004 0.000 0.000 0.380 0.076
#> SRR1090556 5 0.2070 0.8572 0.092 0.000 0.000 0.000 0.896 0.012
#> SRR1357389 3 0.0291 0.7860 0.000 0.000 0.992 0.000 0.004 0.004
#> SRR1404227 6 0.0405 0.7112 0.000 0.004 0.008 0.000 0.000 0.988
#> SRR1376830 1 0.0937 0.8639 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR1500661 1 0.0000 0.8824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1080294 4 0.0260 0.9190 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1336314 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1102152 1 0.5170 0.3904 0.592 0.312 0.000 0.000 0.088 0.008
#> SRR1345244 6 0.4676 0.0984 0.000 0.000 0.428 0.000 0.044 0.528
#> SRR1478637 6 0.1793 0.7069 0.004 0.032 0.000 0.000 0.036 0.928
#> SRR1443776 6 0.3794 0.4697 0.000 0.000 0.248 0.000 0.028 0.724
#> SRR1120939 3 0.4034 0.4343 0.000 0.000 0.652 0.000 0.020 0.328
#> SRR1080117 6 0.4676 0.0984 0.000 0.000 0.428 0.000 0.044 0.528
#> SRR1102899 2 0.0458 0.9211 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1091865 1 0.3650 0.5632 0.716 0.004 0.000 0.000 0.272 0.008
#> SRR1361072 5 0.2996 0.8519 0.228 0.000 0.000 0.000 0.772 0.000
#> SRR1487890 1 0.0000 0.8824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.1116 0.7045 0.000 0.004 0.008 0.000 0.028 0.960
#> SRR1389384 2 0.6581 0.3177 0.156 0.492 0.000 0.000 0.068 0.284
#> SRR1316096 2 0.0363 0.9225 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1408512 5 0.2871 0.8758 0.192 0.000 0.000 0.000 0.804 0.004
#> SRR1447547 4 0.3566 0.7485 0.000 0.000 0.156 0.788 0.056 0.000
#> SRR1354053 4 0.0146 0.9207 0.000 0.004 0.000 0.996 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.687 0.858 0.922 0.4882 0.502 0.502
#> 3 3 0.859 0.922 0.962 0.2311 0.826 0.677
#> 4 4 0.663 0.698 0.867 0.1712 0.881 0.706
#> 5 5 0.646 0.487 0.723 0.0794 0.896 0.687
#> 6 6 0.682 0.675 0.739 0.0456 0.839 0.469
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.962 1.000 0.000
#> SRR1349562 1 0.0000 0.962 1.000 0.000
#> SRR1353376 2 0.0938 0.877 0.012 0.988
#> SRR1499040 2 0.7139 0.798 0.196 0.804
#> SRR1322312 1 0.0000 0.962 1.000 0.000
#> SRR1324412 2 0.3584 0.897 0.068 0.932
#> SRR1100991 2 0.6801 0.823 0.180 0.820
#> SRR1349479 2 0.0000 0.876 0.000 1.000
#> SRR1431248 1 0.0000 0.962 1.000 0.000
#> SRR1405054 1 0.0000 0.962 1.000 0.000
#> SRR1312266 1 0.0000 0.962 1.000 0.000
#> SRR1409790 2 0.3584 0.897 0.068 0.932
#> SRR1352507 2 0.9754 0.481 0.408 0.592
#> SRR1383763 1 0.0000 0.962 1.000 0.000
#> SRR1468314 2 0.0000 0.876 0.000 1.000
#> SRR1473674 2 0.6438 0.817 0.164 0.836
#> SRR1390499 1 0.0000 0.962 1.000 0.000
#> SRR821043 2 0.0000 0.876 0.000 1.000
#> SRR1455653 2 0.5946 0.802 0.144 0.856
#> SRR1335236 2 0.0000 0.876 0.000 1.000
#> SRR1095383 2 0.0000 0.876 0.000 1.000
#> SRR1479489 1 0.0000 0.962 1.000 0.000
#> SRR1310433 2 0.0000 0.876 0.000 1.000
#> SRR1073435 2 0.9977 0.318 0.472 0.528
#> SRR659649 2 0.3584 0.897 0.068 0.932
#> SRR1395999 1 0.0000 0.962 1.000 0.000
#> SRR1105248 2 0.8861 0.610 0.304 0.696
#> SRR1338257 1 0.0000 0.962 1.000 0.000
#> SRR1499395 2 0.3584 0.897 0.068 0.932
#> SRR1350002 2 0.8713 0.618 0.292 0.708
#> SRR1489757 2 0.3733 0.896 0.072 0.928
#> SRR1414637 2 0.9732 0.489 0.404 0.596
#> SRR1478113 2 0.9248 0.528 0.340 0.660
#> SRR1322477 1 0.0000 0.962 1.000 0.000
#> SRR1478789 2 0.3584 0.897 0.068 0.932
#> SRR1414185 2 0.3584 0.897 0.068 0.932
#> SRR1069141 2 0.0000 0.876 0.000 1.000
#> SRR1376852 1 0.0000 0.962 1.000 0.000
#> SRR1323491 1 0.0000 0.962 1.000 0.000
#> SRR1338103 1 0.0000 0.962 1.000 0.000
#> SRR1472012 1 0.8813 0.505 0.700 0.300
#> SRR1340325 1 0.0000 0.962 1.000 0.000
#> SRR1087321 2 0.1184 0.882 0.016 0.984
#> SRR1488790 1 0.0000 0.962 1.000 0.000
#> SRR1334866 2 0.3584 0.897 0.068 0.932
#> SRR1089446 2 0.3584 0.897 0.068 0.932
#> SRR1344445 2 0.3584 0.897 0.068 0.932
#> SRR1412969 2 0.3584 0.897 0.068 0.932
#> SRR1071668 2 0.5178 0.870 0.116 0.884
#> SRR1075804 1 0.0000 0.962 1.000 0.000
#> SRR1383283 2 0.3584 0.897 0.068 0.932
#> SRR1350239 2 0.9460 0.504 0.364 0.636
#> SRR1353878 1 0.0000 0.962 1.000 0.000
#> SRR1375721 1 0.0000 0.962 1.000 0.000
#> SRR1083983 1 0.8144 0.592 0.748 0.252
#> SRR1090095 1 0.0000 0.962 1.000 0.000
#> SRR1414792 1 0.0000 0.962 1.000 0.000
#> SRR1075102 1 0.6801 0.779 0.820 0.180
#> SRR1098737 1 0.0000 0.962 1.000 0.000
#> SRR1349409 1 0.0000 0.962 1.000 0.000
#> SRR1413008 2 0.9460 0.504 0.364 0.636
#> SRR1407179 2 0.9393 0.577 0.356 0.644
#> SRR1095913 2 0.6973 0.812 0.188 0.812
#> SRR1403544 1 0.0000 0.962 1.000 0.000
#> SRR1490546 1 0.0000 0.962 1.000 0.000
#> SRR807971 2 0.3584 0.897 0.068 0.932
#> SRR1436228 1 0.8555 0.550 0.720 0.280
#> SRR1445218 2 0.0000 0.876 0.000 1.000
#> SRR1485438 2 0.6148 0.843 0.152 0.848
#> SRR1358143 1 0.0000 0.962 1.000 0.000
#> SRR1328760 1 0.0000 0.962 1.000 0.000
#> SRR1380806 1 0.0000 0.962 1.000 0.000
#> SRR1379426 2 0.3584 0.897 0.068 0.932
#> SRR1087007 2 0.3431 0.897 0.064 0.936
#> SRR1086256 2 0.3431 0.897 0.064 0.936
#> SRR1346734 2 0.9323 0.511 0.348 0.652
#> SRR1414515 1 0.0000 0.962 1.000 0.000
#> SRR1082151 1 0.0000 0.962 1.000 0.000
#> SRR1349320 2 0.9209 0.535 0.336 0.664
#> SRR1317554 2 0.3879 0.855 0.076 0.924
#> SRR1076022 2 0.0000 0.876 0.000 1.000
#> SRR1339573 2 0.3584 0.897 0.068 0.932
#> SRR1455878 1 0.0000 0.962 1.000 0.000
#> SRR1446203 2 0.3584 0.897 0.068 0.932
#> SRR1387397 1 0.0000 0.962 1.000 0.000
#> SRR1402590 1 0.0000 0.962 1.000 0.000
#> SRR1317532 1 0.0000 0.962 1.000 0.000
#> SRR1331488 1 0.0000 0.962 1.000 0.000
#> SRR1499675 2 0.5294 0.871 0.120 0.880
#> SRR1440467 2 0.2423 0.891 0.040 0.960
#> SRR807995 2 0.3431 0.894 0.064 0.936
#> SRR1476485 2 0.9963 0.199 0.464 0.536
#> SRR1388214 1 0.0000 0.962 1.000 0.000
#> SRR1456051 1 0.0000 0.962 1.000 0.000
#> SRR1473275 2 0.3584 0.897 0.068 0.932
#> SRR1444083 1 0.0000 0.962 1.000 0.000
#> SRR1313807 2 0.6887 0.774 0.184 0.816
#> SRR1470751 1 0.0376 0.958 0.996 0.004
#> SRR1403434 2 0.1843 0.887 0.028 0.972
#> SRR1390540 1 0.0000 0.962 1.000 0.000
#> SRR1093861 2 0.2236 0.890 0.036 0.964
#> SRR1325290 2 0.4022 0.892 0.080 0.920
#> SRR1070689 1 0.0000 0.962 1.000 0.000
#> SRR1384049 1 0.0000 0.962 1.000 0.000
#> SRR1081184 1 0.0000 0.962 1.000 0.000
#> SRR1324295 1 0.0000 0.962 1.000 0.000
#> SRR1365313 2 0.3584 0.897 0.068 0.932
#> SRR1321877 2 0.3584 0.897 0.068 0.932
#> SRR815711 2 0.3584 0.897 0.068 0.932
#> SRR1433476 2 0.0938 0.881 0.012 0.988
#> SRR1101883 2 0.3584 0.897 0.068 0.932
#> SRR1433729 2 0.0000 0.876 0.000 1.000
#> SRR1341877 1 0.0000 0.962 1.000 0.000
#> SRR1090556 1 0.0000 0.962 1.000 0.000
#> SRR1357389 2 0.3584 0.897 0.068 0.932
#> SRR1404227 2 0.3584 0.897 0.068 0.932
#> SRR1376830 1 0.0000 0.962 1.000 0.000
#> SRR1500661 1 0.0000 0.962 1.000 0.000
#> SRR1080294 2 0.0000 0.876 0.000 1.000
#> SRR1336314 1 0.9896 0.164 0.560 0.440
#> SRR1102152 1 0.0000 0.962 1.000 0.000
#> SRR1345244 2 0.3584 0.897 0.068 0.932
#> SRR1478637 2 0.3584 0.897 0.068 0.932
#> SRR1443776 2 0.3584 0.897 0.068 0.932
#> SRR1120939 2 0.2948 0.894 0.052 0.948
#> SRR1080117 2 0.3584 0.897 0.068 0.932
#> SRR1102899 2 0.0000 0.876 0.000 1.000
#> SRR1091865 1 0.0000 0.962 1.000 0.000
#> SRR1361072 1 0.0000 0.962 1.000 0.000
#> SRR1487890 1 0.0000 0.962 1.000 0.000
#> SRR1349456 2 0.3584 0.897 0.068 0.932
#> SRR1389384 1 0.9608 0.213 0.616 0.384
#> SRR1316096 2 0.0000 0.876 0.000 1.000
#> SRR1408512 1 0.0000 0.962 1.000 0.000
#> SRR1447547 1 0.5946 0.788 0.856 0.144
#> SRR1354053 2 0.8144 0.676 0.252 0.748
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1353376 2 0.2796 0.871 0.000 0.908 0.092
#> SRR1499040 3 0.3482 0.823 0.128 0.000 0.872
#> SRR1322312 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1100991 3 0.0747 0.934 0.016 0.000 0.984
#> SRR1349479 3 0.5138 0.619 0.000 0.252 0.748
#> SRR1431248 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1405054 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1352507 3 0.1529 0.916 0.040 0.000 0.960
#> SRR1383763 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1468314 3 0.2959 0.877 0.000 0.100 0.900
#> SRR1473674 3 0.3989 0.852 0.012 0.124 0.864
#> SRR1390499 1 0.0000 0.987 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1335236 3 0.3340 0.861 0.000 0.120 0.880
#> SRR1095383 2 0.6079 0.476 0.000 0.612 0.388
#> SRR1479489 1 0.2066 0.911 0.940 0.000 0.060
#> SRR1310433 3 0.3412 0.858 0.000 0.124 0.876
#> SRR1073435 1 0.5968 0.379 0.636 0.000 0.364
#> SRR659649 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1105248 2 0.3619 0.844 0.000 0.864 0.136
#> SRR1338257 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1350002 3 0.5875 0.771 0.056 0.160 0.784
#> SRR1489757 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1414637 3 0.1529 0.916 0.040 0.000 0.960
#> SRR1478113 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1478789 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1069141 3 0.3551 0.851 0.000 0.132 0.868
#> SRR1376852 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1338103 1 0.0237 0.982 0.996 0.000 0.004
#> SRR1472012 3 0.5178 0.632 0.256 0.000 0.744
#> SRR1340325 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1334866 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1089446 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1071668 3 0.0424 0.939 0.008 0.000 0.992
#> SRR1075804 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1383283 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1350239 2 0.5012 0.787 0.008 0.788 0.204
#> SRR1353878 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1083983 3 0.3551 0.822 0.132 0.000 0.868
#> SRR1090095 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1075102 2 0.0237 0.906 0.004 0.996 0.000
#> SRR1098737 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1413008 2 0.5012 0.787 0.008 0.788 0.204
#> SRR1407179 3 0.4702 0.707 0.212 0.000 0.788
#> SRR1095913 3 0.1411 0.919 0.036 0.000 0.964
#> SRR1403544 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.987 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1436228 3 0.4002 0.780 0.160 0.000 0.840
#> SRR1445218 3 0.5058 0.714 0.000 0.244 0.756
#> SRR1485438 3 0.0424 0.939 0.008 0.000 0.992
#> SRR1358143 1 0.1643 0.933 0.956 0.000 0.044
#> SRR1328760 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1086256 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1346734 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1082151 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1349320 2 0.1163 0.900 0.000 0.972 0.028
#> SRR1317554 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1076022 3 0.3412 0.858 0.000 0.124 0.876
#> SRR1339573 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1331488 1 0.2878 0.886 0.904 0.096 0.000
#> SRR1499675 3 0.1163 0.926 0.028 0.000 0.972
#> SRR1440467 3 0.0000 0.943 0.000 0.000 1.000
#> SRR807995 3 0.3826 0.853 0.008 0.124 0.868
#> SRR1476485 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1313807 3 0.0892 0.931 0.020 0.000 0.980
#> SRR1470751 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1403434 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1093861 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1325290 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1070689 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1365313 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1321877 3 0.0000 0.943 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1433476 3 0.0237 0.941 0.000 0.004 0.996
#> SRR1101883 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1433729 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1341877 1 0.0237 0.982 0.996 0.000 0.004
#> SRR1090556 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1080294 3 0.2261 0.891 0.000 0.068 0.932
#> SRR1336314 2 0.0000 0.907 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1478637 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1443776 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1102899 3 0.1411 0.924 0.000 0.036 0.964
#> SRR1091865 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.943 0.000 0.000 1.000
#> SRR1389384 3 0.4002 0.775 0.160 0.000 0.840
#> SRR1316096 3 0.3816 0.835 0.000 0.148 0.852
#> SRR1408512 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1447547 2 0.8518 0.655 0.208 0.612 0.180
#> SRR1354053 2 0.0000 0.907 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1349562 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1353376 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1499040 2 0.6560 0.5124 0.132 0.620 0.248 0.000
#> SRR1322312 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1324412 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.4188 0.5282 0.004 0.244 0.752 0.000
#> SRR1349479 3 0.3172 0.6242 0.000 0.000 0.840 0.160
#> SRR1431248 1 0.0592 0.9176 0.984 0.016 0.000 0.000
#> SRR1405054 1 0.4819 0.4795 0.652 0.004 0.344 0.000
#> SRR1312266 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1409790 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0895 0.7272 0.020 0.004 0.976 0.000
#> SRR1383763 1 0.0707 0.9190 0.980 0.020 0.000 0.000
#> SRR1468314 3 0.4761 0.2093 0.000 0.372 0.628 0.000
#> SRR1473674 2 0.3649 0.5571 0.000 0.796 0.204 0.000
#> SRR1390499 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR821043 4 0.0336 0.8892 0.000 0.008 0.000 0.992
#> SRR1455653 4 0.0707 0.8844 0.000 0.020 0.000 0.980
#> SRR1335236 2 0.0592 0.6538 0.000 0.984 0.016 0.000
#> SRR1095383 4 0.5105 0.2218 0.000 0.004 0.432 0.564
#> SRR1479489 1 0.5004 0.4574 0.604 0.392 0.004 0.000
#> SRR1310433 2 0.4304 0.4659 0.000 0.716 0.284 0.000
#> SRR1073435 3 0.5883 0.3703 0.060 0.300 0.640 0.000
#> SRR659649 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.4855 0.4303 0.600 0.400 0.000 0.000
#> SRR1105248 4 0.1211 0.8690 0.000 0.000 0.040 0.960
#> SRR1338257 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1499395 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.1489 0.6502 0.004 0.952 0.044 0.000
#> SRR1489757 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1414637 2 0.2412 0.6695 0.008 0.908 0.084 0.000
#> SRR1478113 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1322477 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1478789 3 0.4977 -0.0347 0.000 0.460 0.540 0.000
#> SRR1414185 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.4222 0.4807 0.000 0.728 0.272 0.000
#> SRR1376852 1 0.3610 0.7655 0.800 0.200 0.000 0.000
#> SRR1323491 1 0.0188 0.9227 0.996 0.004 0.000 0.000
#> SRR1338103 1 0.4543 0.5729 0.676 0.324 0.000 0.000
#> SRR1472012 2 0.6586 0.5059 0.184 0.632 0.184 0.000
#> SRR1340325 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1087321 3 0.3907 0.5270 0.000 0.232 0.768 0.000
#> SRR1488790 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1334866 3 0.4999 -0.1236 0.000 0.492 0.508 0.000
#> SRR1089446 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.2081 0.7012 0.000 0.084 0.916 0.000
#> SRR1412969 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1071668 3 0.4008 0.5322 0.000 0.244 0.756 0.000
#> SRR1075804 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1383283 3 0.4406 0.4577 0.000 0.300 0.700 0.000
#> SRR1350239 4 0.3850 0.7278 0.004 0.004 0.188 0.804
#> SRR1353878 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1375721 1 0.3975 0.7132 0.760 0.240 0.000 0.000
#> SRR1083983 2 0.6262 0.4974 0.092 0.628 0.280 0.000
#> SRR1090095 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1414792 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1075102 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1349409 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1413008 4 0.3933 0.7175 0.004 0.004 0.196 0.796
#> SRR1407179 2 0.6576 0.4967 0.200 0.632 0.168 0.000
#> SRR1095913 2 0.4837 0.3977 0.004 0.648 0.348 0.000
#> SRR1403544 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1490546 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR807971 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.5627 0.5656 0.068 0.692 0.240 0.000
#> SRR1445218 2 0.3208 0.6010 0.000 0.848 0.148 0.004
#> SRR1485438 2 0.2281 0.6703 0.000 0.904 0.096 0.000
#> SRR1358143 1 0.4401 0.6681 0.724 0.272 0.004 0.000
#> SRR1328760 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1380806 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1379426 3 0.0817 0.7335 0.000 0.024 0.976 0.000
#> SRR1087007 3 0.4103 0.4870 0.000 0.256 0.744 0.000
#> SRR1086256 2 0.2281 0.6703 0.000 0.904 0.096 0.000
#> SRR1346734 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.3123 0.8114 0.844 0.156 0.000 0.000
#> SRR1082151 1 0.3219 0.7864 0.836 0.164 0.000 0.000
#> SRR1349320 4 0.0188 0.8893 0.000 0.004 0.000 0.996
#> SRR1317554 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1076022 2 0.0592 0.6538 0.000 0.984 0.016 0.000
#> SRR1339573 3 0.3837 0.5383 0.000 0.224 0.776 0.000
#> SRR1455878 1 0.3764 0.7352 0.784 0.216 0.000 0.000
#> SRR1446203 3 0.2589 0.6706 0.000 0.116 0.884 0.000
#> SRR1387397 1 0.0336 0.9227 0.992 0.008 0.000 0.000
#> SRR1402590 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1317532 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1331488 1 0.2714 0.8360 0.884 0.004 0.000 0.112
#> SRR1499675 3 0.4584 0.4448 0.004 0.300 0.696 0.000
#> SRR1440467 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR807995 2 0.0469 0.6523 0.000 0.988 0.012 0.000
#> SRR1476485 4 0.0000 0.8904 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1456051 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1473275 2 0.4804 0.3850 0.000 0.616 0.384 0.000
#> SRR1444083 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1313807 3 0.3972 0.5984 0.008 0.204 0.788 0.000
#> SRR1470751 1 0.3123 0.7947 0.844 0.156 0.000 0.000
#> SRR1403434 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0188 0.9227 0.996 0.004 0.000 0.000
#> SRR1093861 2 0.2281 0.6703 0.000 0.904 0.096 0.000
#> SRR1325290 2 0.4790 0.3932 0.000 0.620 0.380 0.000
#> SRR1070689 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1384049 1 0.0336 0.9228 0.992 0.008 0.000 0.000
#> SRR1081184 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1324295 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1365313 2 0.4877 0.3316 0.000 0.592 0.408 0.000
#> SRR1321877 3 0.4661 0.2631 0.000 0.348 0.652 0.000
#> SRR815711 3 0.3942 0.5435 0.000 0.236 0.764 0.000
#> SRR1433476 3 0.0592 0.7330 0.000 0.000 0.984 0.016
#> SRR1101883 3 0.1389 0.7235 0.000 0.048 0.952 0.000
#> SRR1433729 3 0.0895 0.7313 0.000 0.004 0.976 0.020
#> SRR1341877 1 0.0336 0.9214 0.992 0.008 0.000 0.000
#> SRR1090556 1 0.2921 0.8215 0.860 0.140 0.000 0.000
#> SRR1357389 3 0.0000 0.7406 0.000 0.000 1.000 0.000
#> SRR1404227 2 0.4790 0.3932 0.000 0.620 0.380 0.000
#> SRR1376830 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1500661 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1080294 3 0.0779 0.7362 0.000 0.016 0.980 0.004
#> SRR1336314 4 0.0921 0.8815 0.000 0.028 0.000 0.972
#> SRR1102152 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1345244 3 0.3801 0.5430 0.000 0.220 0.780 0.000
#> SRR1478637 2 0.4790 0.3932 0.000 0.620 0.380 0.000
#> SRR1443776 3 0.4356 0.4112 0.000 0.292 0.708 0.000
#> SRR1120939 3 0.4008 0.5322 0.000 0.244 0.756 0.000
#> SRR1080117 3 0.3907 0.5276 0.000 0.232 0.768 0.000
#> SRR1102899 2 0.4713 0.4133 0.000 0.640 0.360 0.000
#> SRR1091865 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1361072 1 0.0188 0.9221 0.996 0.004 0.000 0.000
#> SRR1487890 1 0.0469 0.9226 0.988 0.012 0.000 0.000
#> SRR1349456 3 0.4776 0.2049 0.000 0.376 0.624 0.000
#> SRR1389384 3 0.7395 -0.1083 0.176 0.344 0.480 0.000
#> SRR1316096 2 0.4277 0.4660 0.000 0.720 0.280 0.000
#> SRR1408512 1 0.4661 0.5371 0.652 0.348 0.000 0.000
#> SRR1447547 4 0.7044 0.5085 0.168 0.004 0.240 0.588
#> SRR1354053 4 0.2011 0.8552 0.000 0.080 0.000 0.920
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1349562 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.1282 0.8813 0.000 0.004 0.000 0.952 0.044
#> SRR1499040 3 0.8150 0.2425 0.112 0.248 0.372 0.000 0.268
#> SRR1322312 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.1704 0.6632 0.068 0.000 0.928 0.000 0.004
#> SRR1100991 3 0.3506 0.6647 0.040 0.036 0.856 0.000 0.068
#> SRR1349479 3 0.5686 0.4272 0.000 0.004 0.624 0.116 0.256
#> SRR1431248 1 0.4549 0.0468 0.528 0.008 0.000 0.000 0.464
#> SRR1405054 5 0.6665 0.3187 0.300 0.000 0.260 0.000 0.440
#> SRR1312266 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1409790 3 0.1410 0.6777 0.000 0.000 0.940 0.000 0.060
#> SRR1352507 3 0.3684 0.3664 0.000 0.000 0.720 0.000 0.280
#> SRR1383763 1 0.1282 0.5350 0.952 0.004 0.000 0.000 0.044
#> SRR1468314 3 0.5028 0.0810 0.000 0.444 0.524 0.000 0.032
#> SRR1473674 2 0.1251 0.7986 0.000 0.956 0.036 0.000 0.008
#> SRR1390499 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.0771 0.8831 0.000 0.020 0.000 0.976 0.004
#> SRR1455653 4 0.1485 0.8727 0.000 0.032 0.000 0.948 0.020
#> SRR1335236 2 0.0162 0.8003 0.000 0.996 0.000 0.000 0.004
#> SRR1095383 4 0.5180 0.5950 0.000 0.004 0.260 0.664 0.072
#> SRR1479489 1 0.3573 0.3550 0.812 0.036 0.000 0.000 0.152
#> SRR1310433 2 0.2104 0.7740 0.000 0.916 0.060 0.000 0.024
#> SRR1073435 5 0.5652 -0.2185 0.004 0.068 0.404 0.000 0.524
#> SRR659649 3 0.0162 0.6934 0.000 0.000 0.996 0.000 0.004
#> SRR1395999 5 0.4921 0.3798 0.340 0.040 0.000 0.000 0.620
#> SRR1105248 4 0.2264 0.8705 0.000 0.004 0.024 0.912 0.060
#> SRR1338257 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1499395 3 0.0162 0.6919 0.000 0.000 0.996 0.000 0.004
#> SRR1350002 2 0.0566 0.8017 0.000 0.984 0.012 0.000 0.004
#> SRR1489757 3 0.0162 0.6919 0.000 0.000 0.996 0.000 0.004
#> SRR1414637 2 0.5798 0.5691 0.000 0.556 0.108 0.000 0.336
#> SRR1478113 4 0.0000 0.8826 0.000 0.000 0.000 1.000 0.000
#> SRR1322477 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1478789 3 0.6297 0.4495 0.000 0.212 0.532 0.000 0.256
#> SRR1414185 3 0.2929 0.6224 0.000 0.000 0.820 0.000 0.180
#> SRR1069141 2 0.1341 0.7917 0.000 0.944 0.056 0.000 0.000
#> SRR1376852 1 0.3134 0.4272 0.848 0.032 0.000 0.000 0.120
#> SRR1323491 1 0.4171 0.1417 0.604 0.000 0.000 0.000 0.396
#> SRR1338103 5 0.5976 0.4170 0.168 0.072 0.084 0.000 0.676
#> SRR1472012 1 0.8424 -0.3364 0.308 0.260 0.152 0.000 0.280
#> SRR1340325 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.3810 0.6282 0.000 0.168 0.792 0.000 0.040
#> SRR1488790 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 3 0.6362 0.4410 0.000 0.224 0.520 0.000 0.256
#> SRR1089446 3 0.2929 0.6224 0.000 0.000 0.820 0.000 0.180
#> SRR1344445 3 0.2595 0.6955 0.000 0.032 0.888 0.000 0.080
#> SRR1412969 3 0.2929 0.6224 0.000 0.000 0.820 0.000 0.180
#> SRR1071668 3 0.2228 0.6893 0.000 0.048 0.912 0.000 0.040
#> SRR1075804 1 0.4126 0.1820 0.620 0.000 0.000 0.000 0.380
#> SRR1383283 3 0.5660 0.5314 0.000 0.124 0.612 0.000 0.264
#> SRR1350239 4 0.5243 0.7366 0.000 0.004 0.104 0.684 0.208
#> SRR1353878 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1375721 1 0.2612 0.4251 0.868 0.008 0.000 0.000 0.124
#> SRR1083983 1 0.7404 -0.0550 0.504 0.100 0.128 0.000 0.268
#> SRR1090095 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.1270 0.8785 0.000 0.000 0.000 0.948 0.052
#> SRR1098737 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1349409 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.5125 0.7283 0.000 0.004 0.176 0.704 0.116
#> SRR1407179 1 0.8441 -0.3308 0.304 0.260 0.156 0.000 0.280
#> SRR1095913 2 0.6351 0.3991 0.000 0.516 0.280 0.000 0.204
#> SRR1403544 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR807971 3 0.0609 0.6952 0.000 0.000 0.980 0.000 0.020
#> SRR1436228 2 0.7062 0.1337 0.016 0.384 0.224 0.000 0.376
#> SRR1445218 2 0.1211 0.7903 0.000 0.960 0.016 0.000 0.024
#> SRR1485438 2 0.3546 0.7595 0.004 0.832 0.116 0.000 0.048
#> SRR1358143 1 0.2771 0.4162 0.860 0.012 0.000 0.000 0.128
#> SRR1328760 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1380806 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.0703 0.6960 0.000 0.000 0.976 0.000 0.024
#> SRR1087007 3 0.5032 0.5973 0.000 0.168 0.704 0.000 0.128
#> SRR1086256 2 0.4808 0.6603 0.000 0.724 0.108 0.000 0.168
#> SRR1346734 4 0.0000 0.8826 0.000 0.000 0.000 1.000 0.000
#> SRR1414515 1 0.2230 0.4432 0.884 0.000 0.000 0.000 0.116
#> SRR1082151 1 0.5114 -0.0831 0.492 0.036 0.000 0.000 0.472
#> SRR1349320 4 0.0290 0.8835 0.000 0.008 0.000 0.992 0.000
#> SRR1317554 4 0.0703 0.8811 0.000 0.000 0.000 0.976 0.024
#> SRR1076022 2 0.2153 0.7980 0.000 0.916 0.040 0.000 0.044
#> SRR1339573 3 0.4989 0.6000 0.000 0.168 0.708 0.000 0.124
#> SRR1455878 5 0.4446 0.2645 0.400 0.008 0.000 0.000 0.592
#> SRR1446203 3 0.2773 0.6924 0.000 0.020 0.868 0.000 0.112
#> SRR1387397 1 0.1965 0.5045 0.904 0.000 0.000 0.000 0.096
#> SRR1402590 1 0.0290 0.5509 0.992 0.000 0.000 0.000 0.008
#> SRR1317532 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1331488 1 0.5603 -0.0917 0.476 0.000 0.000 0.072 0.452
#> SRR1499675 3 0.5245 0.6049 0.000 0.064 0.608 0.000 0.328
#> SRR1440467 3 0.2929 0.6224 0.000 0.000 0.820 0.000 0.180
#> SRR807995 2 0.0162 0.8003 0.000 0.996 0.000 0.000 0.004
#> SRR1476485 4 0.1197 0.8794 0.000 0.000 0.000 0.952 0.048
#> SRR1388214 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1456051 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 3 0.6596 0.3821 0.000 0.256 0.464 0.000 0.280
#> SRR1444083 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1313807 3 0.4735 0.5906 0.000 0.044 0.672 0.000 0.284
#> SRR1470751 1 0.5047 -0.0720 0.496 0.032 0.000 0.000 0.472
#> SRR1403434 3 0.2929 0.6224 0.000 0.000 0.820 0.000 0.180
#> SRR1390540 1 0.3857 0.2651 0.688 0.000 0.000 0.000 0.312
#> SRR1093861 2 0.3339 0.7561 0.000 0.840 0.112 0.000 0.048
#> SRR1325290 3 0.6610 0.3770 0.000 0.260 0.460 0.000 0.280
#> SRR1070689 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.1341 0.5306 0.944 0.000 0.000 0.000 0.056
#> SRR1081184 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.3210 0.4000 0.788 0.000 0.000 0.000 0.212
#> SRR1365313 3 0.6532 0.4031 0.000 0.240 0.480 0.000 0.280
#> SRR1321877 3 0.6120 0.4731 0.000 0.196 0.564 0.000 0.240
#> SRR815711 3 0.4119 0.6123 0.000 0.036 0.752 0.000 0.212
#> SRR1433476 3 0.3109 0.6154 0.000 0.000 0.800 0.000 0.200
#> SRR1101883 3 0.2677 0.6907 0.000 0.016 0.872 0.000 0.112
#> SRR1433729 3 0.2511 0.6906 0.000 0.024 0.908 0.024 0.044
#> SRR1341877 5 0.5447 0.3003 0.400 0.000 0.064 0.000 0.536
#> SRR1090556 5 0.4656 0.0205 0.480 0.012 0.000 0.000 0.508
#> SRR1357389 3 0.1792 0.6675 0.000 0.000 0.916 0.000 0.084
#> SRR1404227 3 0.6610 0.3770 0.000 0.260 0.460 0.000 0.280
#> SRR1376830 1 0.0510 0.5485 0.984 0.000 0.000 0.000 0.016
#> SRR1500661 1 0.0510 0.5487 0.984 0.000 0.000 0.000 0.016
#> SRR1080294 3 0.3154 0.6828 0.000 0.012 0.836 0.004 0.148
#> SRR1336314 4 0.0880 0.8779 0.000 0.032 0.000 0.968 0.000
#> SRR1102152 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1345244 3 0.3810 0.6283 0.000 0.168 0.792 0.000 0.040
#> SRR1478637 3 0.6610 0.3770 0.000 0.260 0.460 0.000 0.280
#> SRR1443776 3 0.5102 0.5908 0.000 0.176 0.696 0.000 0.128
#> SRR1120939 3 0.2074 0.6909 0.000 0.036 0.920 0.000 0.044
#> SRR1080117 3 0.5032 0.5973 0.000 0.168 0.704 0.000 0.128
#> SRR1102899 2 0.3283 0.7624 0.000 0.832 0.140 0.000 0.028
#> SRR1091865 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1361072 1 0.4291 0.0746 0.536 0.000 0.000 0.000 0.464
#> SRR1487890 1 0.0000 0.5524 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.6265 0.4564 0.000 0.220 0.540 0.000 0.240
#> SRR1389384 5 0.6193 0.3462 0.072 0.044 0.292 0.000 0.592
#> SRR1316096 2 0.1893 0.7834 0.000 0.928 0.048 0.000 0.024
#> SRR1408512 5 0.4671 0.3957 0.332 0.028 0.000 0.000 0.640
#> SRR1447547 4 0.6296 0.5907 0.012 0.000 0.120 0.524 0.344
#> SRR1354053 4 0.3284 0.7962 0.000 0.148 0.000 0.828 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1349562 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1353376 4 0.4316 0.7752 0.100 0.100 0.000 0.768 0.000 0.032
#> SRR1499040 6 0.3364 0.6971 0.012 0.000 0.132 0.000 0.036 0.820
#> SRR1322312 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1324412 3 0.0717 0.7210 0.000 0.000 0.976 0.000 0.016 0.008
#> SRR1100991 3 0.3025 0.6345 0.000 0.000 0.820 0.000 0.024 0.156
#> SRR1349479 3 0.7437 0.2620 0.280 0.080 0.460 0.052 0.000 0.128
#> SRR1431248 5 0.1444 0.7862 0.000 0.000 0.000 0.000 0.928 0.072
#> SRR1405054 5 0.2697 0.6376 0.000 0.000 0.188 0.000 0.812 0.000
#> SRR1312266 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1409790 3 0.0260 0.7246 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1352507 3 0.3370 0.5892 0.000 0.000 0.804 0.000 0.148 0.048
#> SRR1383763 1 0.3828 0.8678 0.560 0.000 0.000 0.000 0.440 0.000
#> SRR1468314 2 0.4682 0.1800 0.000 0.556 0.396 0.000 0.000 0.048
#> SRR1473674 2 0.2402 0.8313 0.000 0.856 0.004 0.000 0.000 0.140
#> SRR1390499 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR821043 4 0.2366 0.7903 0.056 0.024 0.000 0.900 0.000 0.020
#> SRR1455653 4 0.1556 0.7665 0.000 0.080 0.000 0.920 0.000 0.000
#> SRR1335236 2 0.2300 0.8314 0.000 0.856 0.000 0.000 0.000 0.144
#> SRR1095383 4 0.7958 0.6063 0.172 0.112 0.208 0.436 0.000 0.072
#> SRR1479489 1 0.5208 0.7210 0.604 0.000 0.000 0.000 0.248 0.148
#> SRR1310433 2 0.1625 0.7914 0.000 0.928 0.012 0.000 0.000 0.060
#> SRR1073435 6 0.4763 0.5705 0.000 0.004 0.172 0.000 0.136 0.688
#> SRR659649 3 0.1151 0.7227 0.000 0.012 0.956 0.000 0.000 0.032
#> SRR1395999 5 0.2697 0.6819 0.000 0.000 0.000 0.000 0.812 0.188
#> SRR1105248 4 0.6730 0.7308 0.252 0.064 0.024 0.536 0.000 0.124
#> SRR1338257 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499395 3 0.1625 0.7177 0.000 0.012 0.928 0.000 0.000 0.060
#> SRR1350002 2 0.2300 0.8314 0.000 0.856 0.000 0.000 0.000 0.144
#> SRR1489757 3 0.0632 0.7218 0.000 0.000 0.976 0.000 0.000 0.024
#> SRR1414637 6 0.4808 0.0249 0.000 0.360 0.000 0.000 0.064 0.576
#> SRR1478113 4 0.0146 0.7851 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1322477 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1478789 6 0.4004 0.5386 0.000 0.012 0.368 0.000 0.000 0.620
#> SRR1414185 3 0.3617 0.6831 0.144 0.012 0.800 0.000 0.000 0.044
#> SRR1069141 2 0.2446 0.8282 0.000 0.864 0.012 0.000 0.000 0.124
#> SRR1376852 1 0.5104 0.8068 0.540 0.000 0.000 0.000 0.372 0.088
#> SRR1323491 5 0.1387 0.7447 0.068 0.000 0.000 0.000 0.932 0.000
#> SRR1338103 6 0.3835 0.4480 0.012 0.000 0.000 0.000 0.320 0.668
#> SRR1472012 6 0.3058 0.5987 0.124 0.000 0.012 0.000 0.024 0.840
#> SRR1340325 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1087321 3 0.2841 0.6414 0.000 0.012 0.824 0.000 0.000 0.164
#> SRR1488790 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1334866 6 0.3911 0.5424 0.000 0.008 0.368 0.000 0.000 0.624
#> SRR1089446 3 0.3268 0.6820 0.144 0.000 0.812 0.000 0.000 0.044
#> SRR1344445 3 0.3151 0.5916 0.000 0.000 0.748 0.000 0.000 0.252
#> SRR1412969 3 0.3617 0.6831 0.144 0.012 0.800 0.000 0.000 0.044
#> SRR1071668 3 0.2260 0.6759 0.000 0.000 0.860 0.000 0.000 0.140
#> SRR1075804 5 0.2340 0.5858 0.148 0.000 0.000 0.000 0.852 0.000
#> SRR1383283 6 0.3859 0.5980 0.000 0.020 0.288 0.000 0.000 0.692
#> SRR1350239 4 0.7405 0.6916 0.308 0.060 0.064 0.444 0.000 0.124
#> SRR1353878 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1375721 1 0.4420 0.8901 0.604 0.000 0.000 0.000 0.360 0.036
#> SRR1083983 1 0.5540 0.1614 0.504 0.000 0.036 0.000 0.056 0.404
#> SRR1090095 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1414792 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1075102 4 0.5138 0.7597 0.176 0.036 0.000 0.684 0.000 0.104
#> SRR1098737 5 0.0260 0.8191 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1349409 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1413008 4 0.7773 0.6797 0.248 0.060 0.124 0.444 0.000 0.124
#> SRR1407179 6 0.3170 0.6059 0.112 0.000 0.016 0.000 0.032 0.840
#> SRR1095913 6 0.4968 0.4453 0.000 0.248 0.120 0.000 0.000 0.632
#> SRR1403544 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1490546 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR807971 3 0.1444 0.7153 0.000 0.000 0.928 0.000 0.000 0.072
#> SRR1436228 6 0.3426 0.6378 0.000 0.064 0.048 0.000 0.048 0.840
#> SRR1445218 2 0.1610 0.8003 0.000 0.916 0.000 0.000 0.000 0.084
#> SRR1485438 2 0.4555 0.7151 0.000 0.660 0.048 0.000 0.008 0.284
#> SRR1358143 1 0.4563 0.8757 0.604 0.000 0.000 0.000 0.348 0.048
#> SRR1328760 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1380806 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1379426 3 0.1686 0.7161 0.000 0.012 0.924 0.000 0.000 0.064
#> SRR1087007 6 0.4181 0.2689 0.000 0.012 0.476 0.000 0.000 0.512
#> SRR1086256 2 0.3868 0.4089 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1346734 4 0.0363 0.7810 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1414515 1 0.4312 0.8996 0.604 0.000 0.000 0.000 0.368 0.028
#> SRR1082151 5 0.1444 0.7872 0.000 0.000 0.000 0.000 0.928 0.072
#> SRR1349320 4 0.3306 0.7786 0.052 0.044 0.000 0.848 0.000 0.056
#> SRR1317554 4 0.1267 0.7742 0.000 0.060 0.000 0.940 0.000 0.000
#> SRR1076022 2 0.3351 0.7511 0.000 0.712 0.000 0.000 0.000 0.288
#> SRR1339573 3 0.4184 -0.2651 0.000 0.012 0.500 0.000 0.000 0.488
#> SRR1455878 5 0.1387 0.7865 0.000 0.000 0.000 0.000 0.932 0.068
#> SRR1446203 3 0.3558 0.5824 0.000 0.016 0.736 0.000 0.000 0.248
#> SRR1387397 5 0.3857 -0.6819 0.468 0.000 0.000 0.000 0.532 0.000
#> SRR1402590 1 0.3765 0.9199 0.596 0.000 0.000 0.000 0.404 0.000
#> SRR1317532 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1331488 5 0.1391 0.7959 0.016 0.000 0.000 0.040 0.944 0.000
#> SRR1499675 6 0.4302 0.4884 0.028 0.000 0.324 0.000 0.004 0.644
#> SRR1440467 3 0.3617 0.6831 0.144 0.012 0.800 0.000 0.000 0.044
#> SRR807995 2 0.2340 0.8312 0.000 0.852 0.000 0.000 0.000 0.148
#> SRR1476485 4 0.4651 0.7675 0.132 0.032 0.000 0.736 0.000 0.100
#> SRR1388214 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1456051 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1473275 6 0.2854 0.6928 0.000 0.000 0.208 0.000 0.000 0.792
#> SRR1444083 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1313807 6 0.5224 0.5654 0.000 0.060 0.276 0.000 0.036 0.628
#> SRR1470751 5 0.1610 0.7772 0.000 0.000 0.000 0.000 0.916 0.084
#> SRR1403434 3 0.3617 0.6831 0.144 0.012 0.800 0.000 0.000 0.044
#> SRR1390540 5 0.2664 0.5045 0.184 0.000 0.000 0.000 0.816 0.000
#> SRR1093861 2 0.3758 0.7043 0.000 0.668 0.008 0.000 0.000 0.324
#> SRR1325290 6 0.2854 0.6963 0.000 0.000 0.208 0.000 0.000 0.792
#> SRR1070689 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1384049 1 0.3857 0.8171 0.532 0.000 0.000 0.000 0.468 0.000
#> SRR1081184 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1324295 5 0.3578 -0.2222 0.340 0.000 0.000 0.000 0.660 0.000
#> SRR1365313 6 0.3215 0.6722 0.000 0.004 0.240 0.000 0.000 0.756
#> SRR1321877 6 0.4101 0.4683 0.000 0.012 0.408 0.000 0.000 0.580
#> SRR815711 3 0.4196 0.6436 0.144 0.000 0.740 0.000 0.000 0.116
#> SRR1433476 3 0.4573 0.6559 0.144 0.068 0.744 0.000 0.000 0.044
#> SRR1101883 3 0.3175 0.5396 0.000 0.000 0.744 0.000 0.000 0.256
#> SRR1433729 3 0.4789 0.5576 0.012 0.064 0.724 0.024 0.000 0.176
#> SRR1341877 5 0.2664 0.6669 0.000 0.000 0.000 0.000 0.816 0.184
#> SRR1090556 5 0.2772 0.6845 0.000 0.000 0.004 0.000 0.816 0.180
#> SRR1357389 3 0.0622 0.7233 0.000 0.012 0.980 0.000 0.000 0.008
#> SRR1404227 6 0.2793 0.6992 0.000 0.000 0.200 0.000 0.000 0.800
#> SRR1376830 1 0.3782 0.9107 0.588 0.000 0.000 0.000 0.412 0.000
#> SRR1500661 1 0.3797 0.9008 0.580 0.000 0.000 0.000 0.420 0.000
#> SRR1080294 3 0.5037 -0.1756 0.004 0.064 0.524 0.000 0.000 0.408
#> SRR1336314 4 0.0937 0.7754 0.000 0.040 0.000 0.960 0.000 0.000
#> SRR1102152 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1345244 3 0.2912 0.6338 0.000 0.012 0.816 0.000 0.000 0.172
#> SRR1478637 6 0.2454 0.6992 0.000 0.000 0.160 0.000 0.000 0.840
#> SRR1443776 3 0.4076 0.1273 0.000 0.012 0.592 0.000 0.000 0.396
#> SRR1120939 3 0.2941 0.6150 0.000 0.000 0.780 0.000 0.000 0.220
#> SRR1080117 3 0.3967 0.2632 0.000 0.012 0.632 0.000 0.000 0.356
#> SRR1102899 2 0.3675 0.7368 0.004 0.796 0.124 0.000 0.000 0.076
#> SRR1091865 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1361072 5 0.0000 0.8226 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1487890 1 0.3747 0.9271 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1349456 6 0.3659 0.5474 0.000 0.000 0.364 0.000 0.000 0.636
#> SRR1389384 5 0.5113 0.2969 0.000 0.000 0.144 0.000 0.620 0.236
#> SRR1316096 2 0.1967 0.8099 0.000 0.904 0.012 0.000 0.000 0.084
#> SRR1408512 5 0.2912 0.6506 0.000 0.000 0.000 0.000 0.784 0.216
#> SRR1447547 4 0.8192 0.5908 0.336 0.060 0.092 0.340 0.012 0.160
#> SRR1354053 4 0.4595 0.6844 0.040 0.264 0.000 0.676 0.000 0.020
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.958 0.980 0.4899 0.512 0.512
#> 3 3 0.634 0.727 0.869 0.3101 0.759 0.560
#> 4 4 0.745 0.805 0.894 0.1111 0.841 0.601
#> 5 5 0.711 0.682 0.771 0.0798 0.807 0.447
#> 6 6 0.771 0.783 0.865 0.0645 0.934 0.702
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.990 1.000 0.000
#> SRR1349562 1 0.0000 0.990 1.000 0.000
#> SRR1353376 2 0.0000 0.972 0.000 1.000
#> SRR1499040 2 0.9933 0.231 0.452 0.548
#> SRR1322312 1 0.0000 0.990 1.000 0.000
#> SRR1324412 2 0.0000 0.972 0.000 1.000
#> SRR1100991 2 0.1633 0.964 0.024 0.976
#> SRR1349479 2 0.0000 0.972 0.000 1.000
#> SRR1431248 1 0.0000 0.990 1.000 0.000
#> SRR1405054 1 0.0000 0.990 1.000 0.000
#> SRR1312266 1 0.0000 0.990 1.000 0.000
#> SRR1409790 2 0.0000 0.972 0.000 1.000
#> SRR1352507 2 0.1633 0.964 0.024 0.976
#> SRR1383763 1 0.0000 0.990 1.000 0.000
#> SRR1468314 2 0.0000 0.972 0.000 1.000
#> SRR1473674 2 0.0000 0.972 0.000 1.000
#> SRR1390499 1 0.0000 0.990 1.000 0.000
#> SRR821043 2 0.0000 0.972 0.000 1.000
#> SRR1455653 2 0.0000 0.972 0.000 1.000
#> SRR1335236 2 0.0000 0.972 0.000 1.000
#> SRR1095383 2 0.0000 0.972 0.000 1.000
#> SRR1479489 1 0.0000 0.990 1.000 0.000
#> SRR1310433 2 0.0000 0.972 0.000 1.000
#> SRR1073435 2 0.3431 0.935 0.064 0.936
#> SRR659649 2 0.2043 0.959 0.032 0.968
#> SRR1395999 1 0.0000 0.990 1.000 0.000
#> SRR1105248 2 0.0000 0.972 0.000 1.000
#> SRR1338257 1 0.0000 0.990 1.000 0.000
#> SRR1499395 2 0.0000 0.972 0.000 1.000
#> SRR1350002 2 0.0000 0.972 0.000 1.000
#> SRR1489757 2 0.0000 0.972 0.000 1.000
#> SRR1414637 2 0.0000 0.972 0.000 1.000
#> SRR1478113 2 0.0000 0.972 0.000 1.000
#> SRR1322477 1 0.0000 0.990 1.000 0.000
#> SRR1478789 2 0.3879 0.924 0.076 0.924
#> SRR1414185 2 0.0000 0.972 0.000 1.000
#> SRR1069141 2 0.0000 0.972 0.000 1.000
#> SRR1376852 1 0.0000 0.990 1.000 0.000
#> SRR1323491 1 0.0000 0.990 1.000 0.000
#> SRR1338103 1 0.0000 0.990 1.000 0.000
#> SRR1472012 1 0.0000 0.990 1.000 0.000
#> SRR1340325 1 0.0000 0.990 1.000 0.000
#> SRR1087321 2 0.0376 0.971 0.004 0.996
#> SRR1488790 1 0.0000 0.990 1.000 0.000
#> SRR1334866 2 0.2423 0.954 0.040 0.960
#> SRR1089446 2 0.0000 0.972 0.000 1.000
#> SRR1344445 2 0.2423 0.954 0.040 0.960
#> SRR1412969 2 0.0000 0.972 0.000 1.000
#> SRR1071668 2 0.1633 0.964 0.024 0.976
#> SRR1075804 1 0.0000 0.990 1.000 0.000
#> SRR1383283 2 0.2603 0.951 0.044 0.956
#> SRR1350239 2 0.0000 0.972 0.000 1.000
#> SRR1353878 1 0.0000 0.990 1.000 0.000
#> SRR1375721 1 0.0000 0.990 1.000 0.000
#> SRR1083983 1 0.0000 0.990 1.000 0.000
#> SRR1090095 1 0.0000 0.990 1.000 0.000
#> SRR1414792 1 0.0000 0.990 1.000 0.000
#> SRR1075102 2 0.0000 0.972 0.000 1.000
#> SRR1098737 1 0.0000 0.990 1.000 0.000
#> SRR1349409 1 0.0000 0.990 1.000 0.000
#> SRR1413008 2 0.0000 0.972 0.000 1.000
#> SRR1407179 2 0.7602 0.750 0.220 0.780
#> SRR1095913 2 0.0000 0.972 0.000 1.000
#> SRR1403544 1 0.0000 0.990 1.000 0.000
#> SRR1490546 1 0.0000 0.990 1.000 0.000
#> SRR807971 2 0.1633 0.964 0.024 0.976
#> SRR1436228 2 0.7219 0.780 0.200 0.800
#> SRR1445218 2 0.0000 0.972 0.000 1.000
#> SRR1485438 2 0.0938 0.968 0.012 0.988
#> SRR1358143 1 0.0000 0.990 1.000 0.000
#> SRR1328760 1 0.0000 0.990 1.000 0.000
#> SRR1380806 1 0.0000 0.990 1.000 0.000
#> SRR1379426 2 0.0000 0.972 0.000 1.000
#> SRR1087007 2 0.1633 0.964 0.024 0.976
#> SRR1086256 2 0.0000 0.972 0.000 1.000
#> SRR1346734 2 0.0000 0.972 0.000 1.000
#> SRR1414515 1 0.0000 0.990 1.000 0.000
#> SRR1082151 2 0.1633 0.962 0.024 0.976
#> SRR1349320 2 0.0000 0.972 0.000 1.000
#> SRR1317554 2 0.0000 0.972 0.000 1.000
#> SRR1076022 2 0.0000 0.972 0.000 1.000
#> SRR1339573 2 0.1633 0.964 0.024 0.976
#> SRR1455878 1 0.0000 0.990 1.000 0.000
#> SRR1446203 2 0.2043 0.959 0.032 0.968
#> SRR1387397 1 0.0000 0.990 1.000 0.000
#> SRR1402590 1 0.0000 0.990 1.000 0.000
#> SRR1317532 1 0.0000 0.990 1.000 0.000
#> SRR1331488 1 0.1633 0.967 0.976 0.024
#> SRR1499675 2 0.0000 0.972 0.000 1.000
#> SRR1440467 2 0.0000 0.972 0.000 1.000
#> SRR807995 2 0.0000 0.972 0.000 1.000
#> SRR1476485 2 0.0000 0.972 0.000 1.000
#> SRR1388214 1 0.0000 0.990 1.000 0.000
#> SRR1456051 1 0.0000 0.990 1.000 0.000
#> SRR1473275 2 0.5842 0.856 0.140 0.860
#> SRR1444083 1 0.0000 0.990 1.000 0.000
#> SRR1313807 2 0.0000 0.972 0.000 1.000
#> SRR1470751 2 0.0000 0.972 0.000 1.000
#> SRR1403434 2 0.0000 0.972 0.000 1.000
#> SRR1390540 1 0.0000 0.990 1.000 0.000
#> SRR1093861 2 0.0000 0.972 0.000 1.000
#> SRR1325290 1 0.9775 0.243 0.588 0.412
#> SRR1070689 1 0.0000 0.990 1.000 0.000
#> SRR1384049 1 0.0000 0.990 1.000 0.000
#> SRR1081184 1 0.0000 0.990 1.000 0.000
#> SRR1324295 1 0.0000 0.990 1.000 0.000
#> SRR1365313 2 0.6801 0.807 0.180 0.820
#> SRR1321877 2 0.2603 0.951 0.044 0.956
#> SRR815711 2 0.0000 0.972 0.000 1.000
#> SRR1433476 2 0.0000 0.972 0.000 1.000
#> SRR1101883 2 0.1633 0.964 0.024 0.976
#> SRR1433729 2 0.0000 0.972 0.000 1.000
#> SRR1341877 1 0.0000 0.990 1.000 0.000
#> SRR1090556 1 0.0000 0.990 1.000 0.000
#> SRR1357389 2 0.0000 0.972 0.000 1.000
#> SRR1404227 2 0.3879 0.924 0.076 0.924
#> SRR1376830 1 0.0000 0.990 1.000 0.000
#> SRR1500661 1 0.0000 0.990 1.000 0.000
#> SRR1080294 2 0.0000 0.972 0.000 1.000
#> SRR1336314 2 0.0000 0.972 0.000 1.000
#> SRR1102152 1 0.2423 0.952 0.960 0.040
#> SRR1345244 2 0.1633 0.964 0.024 0.976
#> SRR1478637 2 0.7139 0.785 0.196 0.804
#> SRR1443776 2 0.3584 0.931 0.068 0.932
#> SRR1120939 2 0.1633 0.964 0.024 0.976
#> SRR1080117 2 0.1633 0.964 0.024 0.976
#> SRR1102899 2 0.0000 0.972 0.000 1.000
#> SRR1091865 1 0.0000 0.990 1.000 0.000
#> SRR1361072 1 0.0000 0.990 1.000 0.000
#> SRR1487890 1 0.0000 0.990 1.000 0.000
#> SRR1349456 2 0.1633 0.964 0.024 0.976
#> SRR1389384 1 0.2948 0.936 0.948 0.052
#> SRR1316096 2 0.0000 0.972 0.000 1.000
#> SRR1408512 1 0.0000 0.990 1.000 0.000
#> SRR1447547 2 0.0000 0.972 0.000 1.000
#> SRR1354053 2 0.0000 0.972 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1499040 3 0.6526 0.6481 0.036 0.260 0.704
#> SRR1322312 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1349479 2 0.1289 0.7989 0.000 0.968 0.032
#> SRR1431248 1 0.7368 0.3290 0.604 0.044 0.352
#> SRR1405054 1 0.7581 -0.0300 0.496 0.040 0.464
#> SRR1312266 1 0.0592 0.9368 0.988 0.000 0.012
#> SRR1409790 3 0.2066 0.7032 0.000 0.060 0.940
#> SRR1352507 3 0.4931 0.6809 0.000 0.232 0.768
#> SRR1383763 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1468314 2 0.4887 0.6961 0.000 0.772 0.228
#> SRR1473674 2 0.5621 0.5382 0.000 0.692 0.308
#> SRR1390499 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1335236 2 0.5291 0.6280 0.000 0.732 0.268
#> SRR1095383 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1310433 2 0.4605 0.7249 0.000 0.796 0.204
#> SRR1073435 3 0.5873 0.6042 0.004 0.312 0.684
#> SRR659649 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1105248 2 0.1289 0.7989 0.000 0.968 0.032
#> SRR1338257 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1350002 2 0.5058 0.6652 0.000 0.756 0.244
#> SRR1489757 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1414637 3 0.6169 0.5387 0.004 0.360 0.636
#> SRR1478113 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1322477 1 0.9701 -0.0878 0.456 0.260 0.284
#> SRR1478789 3 0.4887 0.6798 0.000 0.228 0.772
#> SRR1414185 3 0.5363 0.4840 0.000 0.276 0.724
#> SRR1069141 2 0.4654 0.7211 0.000 0.792 0.208
#> SRR1376852 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1338103 3 0.6225 0.2889 0.432 0.000 0.568
#> SRR1472012 3 0.6816 0.1611 0.472 0.012 0.516
#> SRR1340325 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1334866 3 0.5465 0.6370 0.000 0.288 0.712
#> SRR1089446 3 0.4504 0.5741 0.000 0.196 0.804
#> SRR1344445 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1412969 3 0.4504 0.5739 0.000 0.196 0.804
#> SRR1071668 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1383283 3 0.5929 0.5961 0.004 0.320 0.676
#> SRR1350239 2 0.1289 0.7989 0.000 0.968 0.032
#> SRR1353878 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1083983 3 0.6274 0.2235 0.456 0.000 0.544
#> SRR1090095 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1413008 2 0.1289 0.7989 0.000 0.968 0.032
#> SRR1407179 3 0.6414 0.6578 0.036 0.248 0.716
#> SRR1095913 3 0.6330 0.4672 0.004 0.396 0.600
#> SRR1403544 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1436228 3 0.6897 0.6120 0.040 0.292 0.668
#> SRR1445218 2 0.4605 0.7249 0.000 0.796 0.204
#> SRR1485438 3 0.6008 0.5817 0.004 0.332 0.664
#> SRR1358143 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1328760 1 0.0237 0.9427 0.996 0.000 0.004
#> SRR1380806 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1379426 3 0.4654 0.6938 0.000 0.208 0.792
#> SRR1087007 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1086256 3 0.6033 0.5763 0.004 0.336 0.660
#> SRR1346734 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1082151 3 0.6318 0.5454 0.008 0.356 0.636
#> SRR1349320 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1076022 2 0.6140 0.2375 0.000 0.596 0.404
#> SRR1339573 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1446203 3 0.3412 0.7244 0.000 0.124 0.876
#> SRR1387397 1 0.1753 0.9061 0.952 0.000 0.048
#> SRR1402590 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1331488 1 0.6008 0.4655 0.628 0.372 0.000
#> SRR1499675 3 0.0424 0.7283 0.000 0.008 0.992
#> SRR1440467 3 0.4931 0.5432 0.000 0.232 0.768
#> SRR807995 2 0.6252 0.0631 0.000 0.556 0.444
#> SRR1476485 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1473275 3 0.4351 0.7129 0.004 0.168 0.828
#> SRR1444083 1 0.2280 0.8981 0.940 0.008 0.052
#> SRR1313807 3 0.5926 0.5319 0.000 0.356 0.644
#> SRR1470751 3 0.6521 0.2105 0.004 0.492 0.504
#> SRR1403434 3 0.4931 0.5432 0.000 0.232 0.768
#> SRR1390540 1 0.1620 0.9214 0.964 0.012 0.024
#> SRR1093861 2 0.6095 0.2852 0.000 0.608 0.392
#> SRR1325290 3 0.6875 0.6504 0.056 0.244 0.700
#> SRR1070689 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1365313 3 0.5098 0.6681 0.000 0.248 0.752
#> SRR1321877 3 0.3686 0.7207 0.000 0.140 0.860
#> SRR815711 3 0.4178 0.6027 0.000 0.172 0.828
#> SRR1433476 2 0.5178 0.6690 0.000 0.744 0.256
#> SRR1101883 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1433729 2 0.4796 0.7181 0.000 0.780 0.220
#> SRR1341877 1 0.4178 0.7636 0.828 0.000 0.172
#> SRR1090556 1 0.5397 0.5852 0.720 0.000 0.280
#> SRR1357389 3 0.0237 0.7275 0.000 0.004 0.996
#> SRR1404227 3 0.3816 0.7183 0.000 0.148 0.852
#> SRR1376830 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1080294 2 0.4399 0.7453 0.000 0.812 0.188
#> SRR1336314 2 0.0000 0.8089 0.000 1.000 0.000
#> SRR1102152 3 0.9735 0.3122 0.316 0.244 0.440
#> SRR1345244 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1478637 3 0.5864 0.6351 0.008 0.288 0.704
#> SRR1443776 3 0.3482 0.7236 0.000 0.128 0.872
#> SRR1120939 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.7284 0.000 0.000 1.000
#> SRR1102899 2 0.4842 0.7017 0.000 0.776 0.224
#> SRR1091865 3 0.9317 0.3329 0.388 0.164 0.448
#> SRR1361072 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9455 1.000 0.000 0.000
#> SRR1349456 3 0.4702 0.6906 0.000 0.212 0.788
#> SRR1389384 3 0.7815 0.6005 0.096 0.260 0.644
#> SRR1316096 2 0.4654 0.7211 0.000 0.792 0.208
#> SRR1408512 1 0.1411 0.9177 0.964 0.000 0.036
#> SRR1447547 2 0.1289 0.7989 0.000 0.968 0.032
#> SRR1354053 2 0.0000 0.8089 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0657 0.9761 0.000 0.012 0.004 0.984
#> SRR1499040 1 0.7387 0.2907 0.520 0.224 0.256 0.000
#> SRR1322312 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.1488 0.8378 0.000 0.032 0.956 0.012
#> SRR1100991 3 0.0188 0.8518 0.000 0.000 0.996 0.004
#> SRR1349479 4 0.1305 0.9464 0.000 0.036 0.004 0.960
#> SRR1431248 1 0.4499 0.7447 0.792 0.160 0.048 0.000
#> SRR1405054 1 0.3498 0.7579 0.832 0.008 0.160 0.000
#> SRR1312266 1 0.0817 0.8937 0.976 0.024 0.000 0.000
#> SRR1409790 3 0.2142 0.8226 0.000 0.056 0.928 0.016
#> SRR1352507 3 0.3946 0.7520 0.004 0.012 0.812 0.172
#> SRR1383763 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1468314 2 0.5168 0.0568 0.004 0.500 0.000 0.496
#> SRR1473674 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1390499 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1455653 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1335236 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1095383 4 0.0657 0.9761 0.000 0.012 0.004 0.984
#> SRR1479489 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1073435 3 0.5654 0.5784 0.028 0.276 0.680 0.016
#> SRR659649 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0188 0.9040 0.996 0.004 0.000 0.000
#> SRR1105248 4 0.0376 0.9738 0.000 0.004 0.004 0.992
#> SRR1338257 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1489757 3 0.0524 0.8498 0.000 0.004 0.988 0.008
#> SRR1414637 2 0.4706 0.7087 0.000 0.748 0.224 0.028
#> SRR1478113 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1322477 1 0.4305 0.7644 0.808 0.160 0.012 0.020
#> SRR1478789 3 0.3808 0.7338 0.012 0.176 0.812 0.000
#> SRR1414185 3 0.5030 0.7086 0.000 0.060 0.752 0.188
#> SRR1069141 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1376852 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.6709 0.4920 0.616 0.212 0.172 0.000
#> SRR1472012 1 0.7026 0.4079 0.572 0.180 0.248 0.000
#> SRR1340325 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0592 0.8481 0.000 0.016 0.984 0.000
#> SRR1488790 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1334866 3 0.5382 0.5875 0.016 0.280 0.688 0.016
#> SRR1089446 3 0.4638 0.7432 0.000 0.060 0.788 0.152
#> SRR1344445 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.4685 0.7404 0.000 0.060 0.784 0.156
#> SRR1071668 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1383283 3 0.5369 0.5625 0.012 0.296 0.676 0.016
#> SRR1350239 4 0.0657 0.9653 0.000 0.012 0.004 0.984
#> SRR1353878 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.6757 0.4866 0.612 0.196 0.192 0.000
#> SRR1090095 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1098737 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.0657 0.9653 0.000 0.012 0.004 0.984
#> SRR1407179 1 0.7445 0.2632 0.508 0.224 0.268 0.000
#> SRR1095913 2 0.5143 0.6816 0.004 0.720 0.244 0.032
#> SRR1403544 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.5069 0.5432 0.016 0.664 0.320 0.000
#> SRR1445218 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1485438 2 0.4040 0.6773 0.000 0.752 0.248 0.000
#> SRR1358143 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.3105 0.7860 0.000 0.004 0.856 0.140
#> SRR1087007 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1086256 2 0.4485 0.6818 0.000 0.740 0.248 0.012
#> SRR1346734 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1414515 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.4599 0.7153 0.000 0.760 0.212 0.028
#> SRR1349320 4 0.0592 0.9740 0.000 0.016 0.000 0.984
#> SRR1317554 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1076022 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1339573 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.2760 0.7866 0.000 0.128 0.872 0.000
#> SRR1387397 1 0.1389 0.8782 0.952 0.048 0.000 0.000
#> SRR1402590 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1331488 1 0.5050 0.3253 0.588 0.004 0.000 0.408
#> SRR1499675 3 0.0188 0.8518 0.000 0.000 0.996 0.004
#> SRR1440467 3 0.4685 0.7404 0.000 0.060 0.784 0.156
#> SRR807995 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1476485 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1388214 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.5929 0.5806 0.108 0.204 0.688 0.000
#> SRR1444083 1 0.2831 0.8189 0.876 0.120 0.004 0.000
#> SRR1313807 3 0.5172 0.6255 0.000 0.260 0.704 0.036
#> SRR1470751 2 0.5174 0.7552 0.000 0.756 0.092 0.152
#> SRR1403434 3 0.4685 0.7404 0.000 0.060 0.784 0.156
#> SRR1390540 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1325290 1 0.7558 0.1970 0.480 0.224 0.296 0.000
#> SRR1070689 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.4095 0.7128 0.016 0.192 0.792 0.000
#> SRR1321877 3 0.3024 0.7701 0.000 0.148 0.852 0.000
#> SRR815711 3 0.4055 0.7768 0.000 0.060 0.832 0.108
#> SRR1433476 4 0.2021 0.9210 0.000 0.056 0.012 0.932
#> SRR1101883 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1433729 4 0.2714 0.8407 0.000 0.112 0.004 0.884
#> SRR1341877 1 0.4352 0.7694 0.816 0.104 0.080 0.000
#> SRR1090556 1 0.4499 0.7447 0.792 0.160 0.048 0.000
#> SRR1357389 3 0.1854 0.8297 0.000 0.048 0.940 0.012
#> SRR1404227 3 0.3450 0.7575 0.008 0.156 0.836 0.000
#> SRR1376830 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.0779 0.9742 0.000 0.016 0.004 0.980
#> SRR1336314 4 0.0469 0.9772 0.000 0.012 0.000 0.988
#> SRR1102152 1 0.6820 0.4552 0.604 0.304 0.032 0.060
#> SRR1345244 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1478637 2 0.5483 0.2262 0.016 0.536 0.448 0.000
#> SRR1443776 3 0.3074 0.7665 0.000 0.152 0.848 0.000
#> SRR1120939 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.8524 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1091865 1 0.5614 0.4882 0.628 0.336 0.036 0.000
#> SRR1361072 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9060 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.3681 0.7375 0.008 0.176 0.816 0.000
#> SRR1389384 2 0.5522 0.6670 0.120 0.732 0.148 0.000
#> SRR1316096 2 0.2081 0.8174 0.000 0.916 0.000 0.084
#> SRR1408512 1 0.0469 0.8998 0.988 0.012 0.000 0.000
#> SRR1447547 4 0.1398 0.9428 0.000 0.040 0.004 0.956
#> SRR1354053 4 0.0469 0.9772 0.000 0.012 0.000 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.1043 0.5584 0.960 0.000 0.000 0.000 0.040
#> SRR1349562 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1353376 4 0.0162 0.9269 0.000 0.000 0.004 0.996 0.000
#> SRR1499040 2 0.7189 0.7210 0.088 0.552 0.200 0.000 0.160
#> SRR1322312 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1324412 3 0.3093 0.8519 0.000 0.000 0.824 0.008 0.168
#> SRR1100991 3 0.0290 0.9068 0.000 0.000 0.992 0.000 0.008
#> SRR1349479 4 0.2719 0.8751 0.000 0.000 0.004 0.852 0.144
#> SRR1431248 1 0.4589 0.4110 0.724 0.212 0.064 0.000 0.000
#> SRR1405054 1 0.5107 -0.1293 0.632 0.040 0.008 0.000 0.320
#> SRR1312266 1 0.0566 0.5940 0.984 0.012 0.004 0.000 0.000
#> SRR1409790 3 0.3246 0.8427 0.000 0.000 0.808 0.008 0.184
#> SRR1352507 3 0.1121 0.8969 0.008 0.016 0.968 0.004 0.004
#> SRR1383763 5 0.4304 0.8329 0.484 0.000 0.000 0.000 0.516
#> SRR1468314 2 0.3515 0.6537 0.000 0.844 0.008 0.084 0.064
#> SRR1473674 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1390499 1 0.4287 -0.6557 0.540 0.000 0.000 0.000 0.460
#> SRR821043 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1455653 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1335236 2 0.1924 0.6972 0.000 0.924 0.008 0.004 0.064
#> SRR1095383 4 0.1928 0.9009 0.000 0.072 0.004 0.920 0.004
#> SRR1479489 5 0.4291 0.8849 0.464 0.000 0.000 0.000 0.536
#> SRR1310433 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1073435 2 0.6956 0.6845 0.096 0.544 0.276 0.000 0.084
#> SRR659649 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.4686 -0.1319 0.644 0.016 0.008 0.000 0.332
#> SRR1105248 4 0.0324 0.9261 0.000 0.000 0.004 0.992 0.004
#> SRR1338257 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1489757 3 0.1410 0.8948 0.000 0.000 0.940 0.000 0.060
#> SRR1414637 2 0.1197 0.7292 0.000 0.952 0.048 0.000 0.000
#> SRR1478113 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1322477 1 0.2536 0.5211 0.868 0.128 0.004 0.000 0.000
#> SRR1478789 2 0.6645 0.7344 0.044 0.588 0.208 0.000 0.160
#> SRR1414185 3 0.3586 0.8344 0.000 0.000 0.792 0.020 0.188
#> SRR1069141 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1376852 5 0.4302 0.8453 0.480 0.000 0.000 0.000 0.520
#> SRR1323491 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1338103 2 0.7320 0.6740 0.160 0.540 0.200 0.000 0.100
#> SRR1472012 2 0.7189 0.7210 0.088 0.552 0.200 0.000 0.160
#> SRR1340325 1 0.4088 -0.2810 0.632 0.000 0.000 0.000 0.368
#> SRR1087321 3 0.0963 0.8715 0.000 0.036 0.964 0.000 0.000
#> SRR1488790 5 0.4235 0.9602 0.424 0.000 0.000 0.000 0.576
#> SRR1334866 2 0.6554 0.7323 0.036 0.588 0.220 0.000 0.156
#> SRR1089446 3 0.3586 0.8344 0.000 0.000 0.792 0.020 0.188
#> SRR1344445 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1412969 3 0.3586 0.8344 0.000 0.000 0.792 0.020 0.188
#> SRR1071668 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1075804 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1383283 2 0.6426 0.7243 0.024 0.584 0.236 0.000 0.156
#> SRR1350239 4 0.2286 0.8941 0.000 0.000 0.004 0.888 0.108
#> SRR1353878 1 0.3913 -0.0820 0.676 0.000 0.000 0.000 0.324
#> SRR1375721 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1083983 2 0.7371 0.7080 0.108 0.536 0.200 0.000 0.156
#> SRR1090095 1 0.0162 0.5962 0.996 0.000 0.000 0.000 0.004
#> SRR1414792 1 0.1121 0.5545 0.956 0.000 0.000 0.000 0.044
#> SRR1075102 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1349409 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1413008 4 0.2233 0.8958 0.000 0.000 0.004 0.892 0.104
#> SRR1407179 2 0.7142 0.7227 0.084 0.556 0.200 0.000 0.160
#> SRR1095913 2 0.1661 0.7269 0.000 0.940 0.036 0.000 0.024
#> SRR1403544 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1490546 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1436228 2 0.6744 0.7333 0.052 0.584 0.204 0.000 0.160
#> SRR1445218 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1485438 2 0.2646 0.7383 0.004 0.868 0.124 0.000 0.004
#> SRR1358143 1 0.4305 -0.7518 0.512 0.000 0.000 0.000 0.488
#> SRR1328760 1 0.3857 -0.0326 0.688 0.000 0.000 0.000 0.312
#> SRR1380806 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1379426 3 0.1285 0.8738 0.000 0.036 0.956 0.004 0.004
#> SRR1087007 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 2 0.1792 0.7357 0.000 0.916 0.084 0.000 0.000
#> SRR1346734 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1414515 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1082151 2 0.2770 0.7341 0.044 0.880 0.076 0.000 0.000
#> SRR1349320 4 0.2561 0.8563 0.000 0.144 0.000 0.856 0.000
#> SRR1317554 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1076022 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1339573 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 1 0.4242 -0.5426 0.572 0.000 0.000 0.000 0.428
#> SRR1446203 2 0.7065 0.5703 0.036 0.448 0.360 0.000 0.156
#> SRR1387397 1 0.4470 -0.0891 0.656 0.008 0.008 0.000 0.328
#> SRR1402590 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1317532 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1331488 4 0.1544 0.8813 0.068 0.000 0.000 0.932 0.000
#> SRR1499675 3 0.0162 0.9071 0.000 0.000 0.996 0.000 0.004
#> SRR1440467 3 0.3586 0.8344 0.000 0.000 0.792 0.020 0.188
#> SRR807995 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1476485 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1456051 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1473275 2 0.6592 0.7357 0.044 0.596 0.200 0.000 0.160
#> SRR1444083 1 0.0566 0.5940 0.984 0.012 0.004 0.000 0.000
#> SRR1313807 2 0.6720 0.5446 0.000 0.492 0.372 0.076 0.060
#> SRR1470751 2 0.3265 0.6599 0.012 0.848 0.020 0.120 0.000
#> SRR1403434 3 0.3586 0.8344 0.000 0.000 0.792 0.020 0.188
#> SRR1390540 1 0.0324 0.5961 0.992 0.000 0.004 0.004 0.000
#> SRR1093861 2 0.1924 0.6972 0.000 0.924 0.008 0.004 0.064
#> SRR1325290 2 0.7189 0.7210 0.088 0.552 0.200 0.000 0.160
#> SRR1070689 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1384049 1 0.3876 -0.0488 0.684 0.000 0.000 0.000 0.316
#> SRR1081184 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1324295 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1365313 2 0.6592 0.7357 0.044 0.596 0.200 0.000 0.160
#> SRR1321877 2 0.6721 0.7143 0.036 0.560 0.244 0.000 0.160
#> SRR815711 3 0.3391 0.8394 0.000 0.000 0.800 0.012 0.188
#> SRR1433476 4 0.3160 0.8455 0.000 0.000 0.004 0.808 0.188
#> SRR1101883 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1433729 4 0.4791 0.2668 0.000 0.392 0.012 0.588 0.008
#> SRR1341877 1 0.3112 0.4952 0.856 0.044 0.100 0.000 0.000
#> SRR1090556 1 0.4950 0.3054 0.612 0.348 0.040 0.000 0.000
#> SRR1357389 3 0.3086 0.8478 0.000 0.000 0.816 0.004 0.180
#> SRR1404227 2 0.6645 0.7344 0.044 0.588 0.208 0.000 0.160
#> SRR1376830 5 0.4287 0.8957 0.460 0.000 0.000 0.000 0.540
#> SRR1500661 1 0.4045 -0.2255 0.644 0.000 0.000 0.000 0.356
#> SRR1080294 4 0.2818 0.8668 0.000 0.128 0.004 0.860 0.008
#> SRR1336314 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
#> SRR1102152 2 0.5180 -0.0236 0.476 0.492 0.012 0.020 0.000
#> SRR1345244 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 2 0.6553 0.7349 0.040 0.596 0.204 0.000 0.160
#> SRR1443776 2 0.6420 0.6569 0.008 0.524 0.308 0.000 0.160
#> SRR1120939 3 0.0798 0.8892 0.000 0.016 0.976 0.000 0.008
#> SRR1080117 3 0.0000 0.9071 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.1831 0.6962 0.000 0.920 0.000 0.004 0.076
#> SRR1091865 1 0.4659 0.0290 0.500 0.488 0.012 0.000 0.000
#> SRR1361072 1 0.0000 0.5990 1.000 0.000 0.000 0.000 0.000
#> SRR1487890 5 0.4227 0.9664 0.420 0.000 0.000 0.000 0.580
#> SRR1349456 2 0.6562 0.7315 0.036 0.588 0.216 0.000 0.160
#> SRR1389384 2 0.6261 0.4972 0.296 0.524 0.180 0.000 0.000
#> SRR1316096 2 0.1768 0.6952 0.000 0.924 0.000 0.004 0.072
#> SRR1408512 1 0.4557 0.3269 0.656 0.324 0.012 0.000 0.008
#> SRR1447547 4 0.3048 0.8545 0.000 0.000 0.004 0.820 0.176
#> SRR1354053 4 0.0000 0.9279 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1349562 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1353376 4 0.0820 0.9214 0.012 0.016 0.000 0.972 0.000 0.000
#> SRR1499040 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1322312 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1324412 3 0.2571 0.8769 0.064 0.000 0.876 0.000 0.000 0.060
#> SRR1100991 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1349479 4 0.3564 0.8686 0.088 0.016 0.076 0.820 0.000 0.000
#> SRR1431248 5 0.2003 0.7758 0.000 0.000 0.000 0.000 0.884 0.116
#> SRR1405054 5 0.4779 0.4291 0.040 0.000 0.008 0.000 0.568 0.384
#> SRR1312266 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1409790 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1352507 3 0.3564 0.8655 0.008 0.000 0.808 0.000 0.060 0.124
#> SRR1383763 1 0.2006 0.9158 0.892 0.000 0.000 0.000 0.104 0.004
#> SRR1468314 2 0.4497 0.3665 0.000 0.624 0.000 0.328 0.000 0.048
#> SRR1473674 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1390499 1 0.3854 0.2066 0.536 0.000 0.000 0.000 0.464 0.000
#> SRR821043 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1455653 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.0547 0.8429 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1095383 4 0.2288 0.8831 0.004 0.116 0.004 0.876 0.000 0.000
#> SRR1479489 1 0.2006 0.9158 0.892 0.000 0.000 0.000 0.104 0.004
#> SRR1310433 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1073435 6 0.4970 0.5499 0.000 0.004 0.120 0.000 0.224 0.652
#> SRR659649 3 0.2378 0.8997 0.000 0.000 0.848 0.000 0.000 0.152
#> SRR1395999 5 0.5176 0.5841 0.188 0.000 0.000 0.000 0.620 0.192
#> SRR1105248 4 0.0964 0.9211 0.012 0.016 0.004 0.968 0.000 0.000
#> SRR1338257 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499395 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1350002 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1489757 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1414637 2 0.3868 -0.0425 0.000 0.504 0.000 0.000 0.000 0.496
#> SRR1478113 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1322477 5 0.0458 0.8114 0.000 0.000 0.000 0.000 0.984 0.016
#> SRR1478789 6 0.0146 0.8470 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1414185 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1069141 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1376852 1 0.2006 0.9158 0.892 0.000 0.000 0.000 0.104 0.004
#> SRR1323491 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1338103 6 0.2266 0.7638 0.012 0.000 0.000 0.000 0.108 0.880
#> SRR1472012 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1340325 1 0.3991 0.1850 0.524 0.000 0.000 0.000 0.472 0.004
#> SRR1087321 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1488790 1 0.3464 0.6041 0.688 0.000 0.000 0.000 0.312 0.000
#> SRR1334866 6 0.0146 0.8470 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1089446 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1344445 3 0.2454 0.8849 0.000 0.000 0.840 0.000 0.000 0.160
#> SRR1412969 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1071668 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1075804 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1383283 6 0.2252 0.8144 0.000 0.028 0.044 0.000 0.020 0.908
#> SRR1350239 4 0.3185 0.8841 0.060 0.016 0.076 0.848 0.000 0.000
#> SRR1353878 5 0.2838 0.6812 0.188 0.000 0.000 0.000 0.808 0.004
#> SRR1375721 1 0.1765 0.9218 0.904 0.000 0.000 0.000 0.096 0.000
#> SRR1083983 6 0.0547 0.8365 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR1090095 5 0.2048 0.7286 0.120 0.000 0.000 0.000 0.880 0.000
#> SRR1414792 5 0.2048 0.7286 0.120 0.000 0.000 0.000 0.880 0.000
#> SRR1075102 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1098737 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1349409 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1413008 4 0.3125 0.8861 0.056 0.016 0.076 0.852 0.000 0.000
#> SRR1407179 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1095913 2 0.3993 0.0263 0.000 0.520 0.004 0.000 0.000 0.476
#> SRR1403544 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1490546 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR807971 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1436228 6 0.0146 0.8461 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1445218 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1485438 6 0.3659 0.4020 0.000 0.364 0.000 0.000 0.000 0.636
#> SRR1358143 1 0.2006 0.9158 0.892 0.000 0.000 0.000 0.104 0.004
#> SRR1328760 5 0.2703 0.6991 0.172 0.000 0.000 0.000 0.824 0.004
#> SRR1380806 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1379426 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1087007 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1086256 6 0.3797 0.2444 0.000 0.420 0.000 0.000 0.000 0.580
#> SRR1346734 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1082151 6 0.5718 0.0406 0.000 0.396 0.000 0.000 0.164 0.440
#> SRR1349320 4 0.2631 0.8125 0.000 0.180 0.000 0.820 0.000 0.000
#> SRR1317554 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1076022 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1339573 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1455878 1 0.3736 0.7951 0.776 0.000 0.000 0.000 0.156 0.068
#> SRR1446203 6 0.2697 0.7010 0.000 0.000 0.188 0.000 0.000 0.812
#> SRR1387397 5 0.4787 0.6283 0.184 0.000 0.000 0.000 0.672 0.144
#> SRR1402590 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1317532 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1331488 4 0.2456 0.8649 0.004 0.012 0.004 0.880 0.100 0.000
#> SRR1499675 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1440467 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR807995 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1476485 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1456051 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1473275 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1444083 5 0.0146 0.8134 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1313807 6 0.6407 0.3781 0.004 0.120 0.300 0.060 0.000 0.516
#> SRR1470751 2 0.6049 0.0640 0.000 0.428 0.000 0.004 0.216 0.352
#> SRR1403434 3 0.1556 0.8452 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1390540 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1093861 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1325290 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1070689 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1384049 5 0.2871 0.6767 0.192 0.000 0.000 0.000 0.804 0.004
#> SRR1081184 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1324295 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1365313 6 0.0146 0.8470 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1321877 6 0.0937 0.8279 0.000 0.000 0.040 0.000 0.000 0.960
#> SRR815711 3 0.1501 0.8466 0.076 0.000 0.924 0.000 0.000 0.000
#> SRR1433476 4 0.4081 0.8376 0.088 0.016 0.120 0.776 0.000 0.000
#> SRR1101883 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1433729 4 0.2531 0.8742 0.008 0.128 0.004 0.860 0.000 0.000
#> SRR1341877 5 0.2969 0.6773 0.000 0.000 0.000 0.000 0.776 0.224
#> SRR1090556 5 0.3714 0.5339 0.004 0.000 0.000 0.000 0.656 0.340
#> SRR1357389 3 0.1141 0.8540 0.052 0.000 0.948 0.000 0.000 0.000
#> SRR1404227 6 0.0146 0.8470 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1376830 1 0.1765 0.9219 0.904 0.000 0.000 0.000 0.096 0.000
#> SRR1500661 5 0.4273 0.3038 0.380 0.000 0.000 0.000 0.596 0.024
#> SRR1080294 4 0.2884 0.8433 0.008 0.164 0.004 0.824 0.000 0.000
#> SRR1336314 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1102152 5 0.4015 0.4290 0.004 0.004 0.000 0.000 0.596 0.396
#> SRR1345244 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1478637 6 0.0000 0.8471 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443776 6 0.1204 0.8193 0.000 0.000 0.056 0.000 0.000 0.944
#> SRR1120939 3 0.2454 0.8871 0.000 0.000 0.840 0.000 0.000 0.160
#> SRR1080117 3 0.2135 0.9146 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1102899 2 0.0603 0.8425 0.000 0.980 0.004 0.000 0.000 0.016
#> SRR1091865 5 0.4033 0.4112 0.004 0.004 0.000 0.000 0.588 0.404
#> SRR1361072 5 0.0000 0.8141 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1487890 1 0.1663 0.9245 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1349456 6 0.0260 0.8459 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1389384 6 0.4990 0.5257 0.000 0.204 0.000 0.000 0.152 0.644
#> SRR1316096 2 0.0458 0.8455 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1408512 5 0.3426 0.6335 0.004 0.000 0.000 0.000 0.720 0.276
#> SRR1447547 4 0.4081 0.8376 0.088 0.016 0.120 0.776 0.000 0.000
#> SRR1354053 4 0.0000 0.9241 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.951 0.980 0.5034 0.496 0.496
#> 3 3 0.920 0.907 0.953 0.3055 0.783 0.588
#> 4 4 0.936 0.909 0.956 0.0940 0.904 0.731
#> 5 5 0.719 0.645 0.798 0.0673 0.974 0.908
#> 6 6 0.687 0.496 0.689 0.0484 0.910 0.682
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.985 1.000 0.000
#> SRR1349562 1 0.0000 0.985 1.000 0.000
#> SRR1353376 2 0.0000 0.974 0.000 1.000
#> SRR1499040 1 0.0000 0.985 1.000 0.000
#> SRR1322312 1 0.0000 0.985 1.000 0.000
#> SRR1324412 1 0.0000 0.985 1.000 0.000
#> SRR1100991 1 0.0000 0.985 1.000 0.000
#> SRR1349479 2 0.0000 0.974 0.000 1.000
#> SRR1431248 1 0.0000 0.985 1.000 0.000
#> SRR1405054 1 0.0000 0.985 1.000 0.000
#> SRR1312266 1 0.0000 0.985 1.000 0.000
#> SRR1409790 1 0.1633 0.962 0.976 0.024
#> SRR1352507 2 0.8499 0.634 0.276 0.724
#> SRR1383763 1 0.0000 0.985 1.000 0.000
#> SRR1468314 2 0.0000 0.974 0.000 1.000
#> SRR1473674 2 0.0000 0.974 0.000 1.000
#> SRR1390499 1 0.0000 0.985 1.000 0.000
#> SRR821043 2 0.0000 0.974 0.000 1.000
#> SRR1455653 2 0.0000 0.974 0.000 1.000
#> SRR1335236 2 0.0000 0.974 0.000 1.000
#> SRR1095383 2 0.0000 0.974 0.000 1.000
#> SRR1479489 1 0.0000 0.985 1.000 0.000
#> SRR1310433 2 0.0000 0.974 0.000 1.000
#> SRR1073435 2 0.0000 0.974 0.000 1.000
#> SRR659649 2 0.0000 0.974 0.000 1.000
#> SRR1395999 1 0.0000 0.985 1.000 0.000
#> SRR1105248 2 0.0000 0.974 0.000 1.000
#> SRR1338257 1 0.0000 0.985 1.000 0.000
#> SRR1499395 2 0.0000 0.974 0.000 1.000
#> SRR1350002 2 0.0000 0.974 0.000 1.000
#> SRR1489757 1 0.9754 0.284 0.592 0.408
#> SRR1414637 2 0.0000 0.974 0.000 1.000
#> SRR1478113 2 0.0000 0.974 0.000 1.000
#> SRR1322477 1 0.0000 0.985 1.000 0.000
#> SRR1478789 2 0.0000 0.974 0.000 1.000
#> SRR1414185 2 0.0000 0.974 0.000 1.000
#> SRR1069141 2 0.0000 0.974 0.000 1.000
#> SRR1376852 1 0.0000 0.985 1.000 0.000
#> SRR1323491 1 0.0000 0.985 1.000 0.000
#> SRR1338103 1 0.0000 0.985 1.000 0.000
#> SRR1472012 1 0.0000 0.985 1.000 0.000
#> SRR1340325 1 0.0000 0.985 1.000 0.000
#> SRR1087321 2 0.0000 0.974 0.000 1.000
#> SRR1488790 1 0.0000 0.985 1.000 0.000
#> SRR1334866 2 0.0000 0.974 0.000 1.000
#> SRR1089446 2 0.0000 0.974 0.000 1.000
#> SRR1344445 1 0.9661 0.329 0.608 0.392
#> SRR1412969 2 0.0000 0.974 0.000 1.000
#> SRR1071668 1 0.0000 0.985 1.000 0.000
#> SRR1075804 1 0.0000 0.985 1.000 0.000
#> SRR1383283 2 0.0000 0.974 0.000 1.000
#> SRR1350239 2 0.0000 0.974 0.000 1.000
#> SRR1353878 1 0.0000 0.985 1.000 0.000
#> SRR1375721 1 0.0000 0.985 1.000 0.000
#> SRR1083983 1 0.0000 0.985 1.000 0.000
#> SRR1090095 1 0.0000 0.985 1.000 0.000
#> SRR1414792 1 0.0000 0.985 1.000 0.000
#> SRR1075102 2 0.0000 0.974 0.000 1.000
#> SRR1098737 1 0.0000 0.985 1.000 0.000
#> SRR1349409 1 0.0000 0.985 1.000 0.000
#> SRR1413008 2 0.0000 0.974 0.000 1.000
#> SRR1407179 1 0.0000 0.985 1.000 0.000
#> SRR1095913 2 0.0000 0.974 0.000 1.000
#> SRR1403544 1 0.0000 0.985 1.000 0.000
#> SRR1490546 1 0.0000 0.985 1.000 0.000
#> SRR807971 1 0.3114 0.929 0.944 0.056
#> SRR1436228 1 0.4022 0.903 0.920 0.080
#> SRR1445218 2 0.0000 0.974 0.000 1.000
#> SRR1485438 2 0.0672 0.967 0.008 0.992
#> SRR1358143 1 0.0000 0.985 1.000 0.000
#> SRR1328760 1 0.0000 0.985 1.000 0.000
#> SRR1380806 1 0.0000 0.985 1.000 0.000
#> SRR1379426 2 0.0000 0.974 0.000 1.000
#> SRR1087007 2 0.0000 0.974 0.000 1.000
#> SRR1086256 2 0.0000 0.974 0.000 1.000
#> SRR1346734 2 0.0000 0.974 0.000 1.000
#> SRR1414515 1 0.0000 0.985 1.000 0.000
#> SRR1082151 2 0.9775 0.321 0.412 0.588
#> SRR1349320 2 0.0000 0.974 0.000 1.000
#> SRR1317554 2 0.0000 0.974 0.000 1.000
#> SRR1076022 2 0.0000 0.974 0.000 1.000
#> SRR1339573 2 0.5408 0.852 0.124 0.876
#> SRR1455878 1 0.0000 0.985 1.000 0.000
#> SRR1446203 2 0.0000 0.974 0.000 1.000
#> SRR1387397 1 0.0000 0.985 1.000 0.000
#> SRR1402590 1 0.0000 0.985 1.000 0.000
#> SRR1317532 1 0.0000 0.985 1.000 0.000
#> SRR1331488 1 0.0000 0.985 1.000 0.000
#> SRR1499675 2 0.1843 0.950 0.028 0.972
#> SRR1440467 2 0.0000 0.974 0.000 1.000
#> SRR807995 2 0.0000 0.974 0.000 1.000
#> SRR1476485 2 0.0000 0.974 0.000 1.000
#> SRR1388214 1 0.0000 0.985 1.000 0.000
#> SRR1456051 1 0.0000 0.985 1.000 0.000
#> SRR1473275 1 0.0000 0.985 1.000 0.000
#> SRR1444083 1 0.0000 0.985 1.000 0.000
#> SRR1313807 2 0.0000 0.974 0.000 1.000
#> SRR1470751 2 0.9286 0.487 0.344 0.656
#> SRR1403434 2 0.0000 0.974 0.000 1.000
#> SRR1390540 1 0.0000 0.985 1.000 0.000
#> SRR1093861 2 0.0000 0.974 0.000 1.000
#> SRR1325290 1 0.0000 0.985 1.000 0.000
#> SRR1070689 1 0.0000 0.985 1.000 0.000
#> SRR1384049 1 0.0000 0.985 1.000 0.000
#> SRR1081184 1 0.0000 0.985 1.000 0.000
#> SRR1324295 1 0.0000 0.985 1.000 0.000
#> SRR1365313 2 0.8909 0.573 0.308 0.692
#> SRR1321877 2 0.0000 0.974 0.000 1.000
#> SRR815711 2 0.0000 0.974 0.000 1.000
#> SRR1433476 2 0.0000 0.974 0.000 1.000
#> SRR1101883 2 0.6247 0.813 0.156 0.844
#> SRR1433729 2 0.0000 0.974 0.000 1.000
#> SRR1341877 1 0.0000 0.985 1.000 0.000
#> SRR1090556 1 0.0000 0.985 1.000 0.000
#> SRR1357389 2 0.4562 0.883 0.096 0.904
#> SRR1404227 2 0.0376 0.970 0.004 0.996
#> SRR1376830 1 0.0000 0.985 1.000 0.000
#> SRR1500661 1 0.0000 0.985 1.000 0.000
#> SRR1080294 2 0.0000 0.974 0.000 1.000
#> SRR1336314 2 0.0000 0.974 0.000 1.000
#> SRR1102152 1 0.0000 0.985 1.000 0.000
#> SRR1345244 2 0.0000 0.974 0.000 1.000
#> SRR1478637 2 0.0000 0.974 0.000 1.000
#> SRR1443776 2 0.0000 0.974 0.000 1.000
#> SRR1120939 2 0.0000 0.974 0.000 1.000
#> SRR1080117 2 0.0000 0.974 0.000 1.000
#> SRR1102899 2 0.0000 0.974 0.000 1.000
#> SRR1091865 1 0.0000 0.985 1.000 0.000
#> SRR1361072 1 0.0000 0.985 1.000 0.000
#> SRR1487890 1 0.0000 0.985 1.000 0.000
#> SRR1349456 2 0.0000 0.974 0.000 1.000
#> SRR1389384 1 0.0000 0.985 1.000 0.000
#> SRR1316096 2 0.0000 0.974 0.000 1.000
#> SRR1408512 1 0.0000 0.985 1.000 0.000
#> SRR1447547 2 0.0000 0.974 0.000 1.000
#> SRR1354053 2 0.0000 0.974 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1353376 2 0.1860 0.906 0.000 0.948 0.052
#> SRR1499040 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1322312 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1324412 3 0.1031 0.934 0.024 0.000 0.976
#> SRR1100991 3 0.2066 0.901 0.060 0.000 0.940
#> SRR1349479 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1431248 1 0.2796 0.901 0.908 0.092 0.000
#> SRR1405054 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1312266 1 0.1964 0.933 0.944 0.056 0.000
#> SRR1409790 3 0.1163 0.931 0.028 0.000 0.972
#> SRR1352507 3 0.1860 0.912 0.000 0.052 0.948
#> SRR1383763 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1468314 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1473674 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1390499 1 0.0000 0.974 1.000 0.000 0.000
#> SRR821043 2 0.1529 0.911 0.000 0.960 0.040
#> SRR1455653 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1335236 2 0.2356 0.915 0.000 0.928 0.072
#> SRR1095383 2 0.1860 0.906 0.000 0.948 0.052
#> SRR1479489 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1310433 2 0.2066 0.921 0.000 0.940 0.060
#> SRR1073435 2 0.3619 0.881 0.000 0.864 0.136
#> SRR659649 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1105248 2 0.1964 0.904 0.000 0.944 0.056
#> SRR1338257 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1350002 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1489757 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1414637 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1478113 2 0.1529 0.911 0.000 0.960 0.040
#> SRR1322477 1 0.5905 0.497 0.648 0.352 0.000
#> SRR1478789 3 0.1860 0.912 0.000 0.052 0.948
#> SRR1414185 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1069141 2 0.1860 0.924 0.000 0.948 0.052
#> SRR1376852 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1334866 2 0.6307 0.129 0.000 0.512 0.488
#> SRR1089446 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1071668 3 0.1753 0.913 0.048 0.000 0.952
#> SRR1075804 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1383283 2 0.2356 0.915 0.000 0.928 0.072
#> SRR1350239 3 0.4346 0.785 0.000 0.184 0.816
#> SRR1353878 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1075102 2 0.1643 0.909 0.000 0.956 0.044
#> SRR1098737 1 0.1860 0.936 0.948 0.052 0.000
#> SRR1349409 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1413008 3 0.4452 0.774 0.000 0.192 0.808
#> SRR1407179 1 0.0424 0.968 0.992 0.000 0.008
#> SRR1095913 2 0.2066 0.921 0.000 0.940 0.060
#> SRR1403544 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.974 1.000 0.000 0.000
#> SRR807971 3 0.0892 0.937 0.020 0.000 0.980
#> SRR1436228 2 0.6299 0.075 0.476 0.524 0.000
#> SRR1445218 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1485438 2 0.2096 0.923 0.004 0.944 0.052
#> SRR1358143 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1379426 3 0.0237 0.948 0.000 0.004 0.996
#> SRR1087007 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1086256 2 0.1860 0.924 0.000 0.948 0.052
#> SRR1346734 2 0.1411 0.911 0.000 0.964 0.036
#> SRR1414515 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1082151 2 0.0475 0.913 0.004 0.992 0.004
#> SRR1349320 2 0.1529 0.911 0.000 0.960 0.040
#> SRR1317554 2 0.1163 0.913 0.000 0.972 0.028
#> SRR1076022 2 0.1860 0.924 0.000 0.948 0.052
#> SRR1339573 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1446203 3 0.0237 0.948 0.000 0.004 0.996
#> SRR1387397 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1331488 1 0.6927 0.547 0.664 0.296 0.040
#> SRR1499675 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1440467 3 0.0000 0.950 0.000 0.000 1.000
#> SRR807995 2 0.1753 0.924 0.000 0.952 0.048
#> SRR1476485 2 0.1643 0.909 0.000 0.956 0.044
#> SRR1388214 1 0.0747 0.963 0.984 0.016 0.000
#> SRR1456051 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1473275 3 0.6291 0.147 0.468 0.000 0.532
#> SRR1444083 1 0.1860 0.936 0.948 0.052 0.000
#> SRR1313807 2 0.3267 0.896 0.000 0.884 0.116
#> SRR1470751 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1403434 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1093861 2 0.2066 0.921 0.000 0.940 0.060
#> SRR1325290 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1365313 3 0.6049 0.693 0.204 0.040 0.756
#> SRR1321877 3 0.0424 0.945 0.000 0.008 0.992
#> SRR815711 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1433476 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1101883 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1433729 2 0.4605 0.801 0.000 0.796 0.204
#> SRR1341877 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1090556 1 0.2066 0.929 0.940 0.060 0.000
#> SRR1357389 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1404227 3 0.1267 0.932 0.024 0.004 0.972
#> SRR1376830 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1080294 2 0.6154 0.429 0.000 0.592 0.408
#> SRR1336314 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1102152 1 0.5529 0.575 0.704 0.296 0.000
#> SRR1345244 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1478637 2 0.1860 0.924 0.000 0.948 0.052
#> SRR1443776 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1102899 2 0.2356 0.915 0.000 0.928 0.072
#> SRR1091865 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1349456 3 0.1529 0.921 0.000 0.040 0.960
#> SRR1389384 1 0.4062 0.802 0.836 0.164 0.000
#> SRR1316096 2 0.1860 0.924 0.000 0.948 0.052
#> SRR1408512 1 0.0000 0.974 1.000 0.000 0.000
#> SRR1447547 3 0.4654 0.750 0.000 0.208 0.792
#> SRR1354053 2 0.0000 0.911 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.2973 0.8068 0.000 0.144 0.000 0.856
#> SRR1499040 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1322312 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0188 0.9570 0.000 0.000 0.996 0.004
#> SRR1100991 3 0.0657 0.9548 0.004 0.000 0.984 0.012
#> SRR1349479 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1431248 1 0.3942 0.6748 0.764 0.000 0.000 0.236
#> SRR1405054 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1312266 4 0.3266 0.7587 0.168 0.000 0.000 0.832
#> SRR1409790 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1352507 3 0.0469 0.9551 0.000 0.000 0.988 0.012
#> SRR1383763 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1468314 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1473674 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0817 0.8644 0.000 0.024 0.000 0.976
#> SRR1455653 2 0.3907 0.6570 0.000 0.768 0.000 0.232
#> SRR1335236 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1095383 4 0.1211 0.8666 0.000 0.040 0.000 0.960
#> SRR1479489 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1073435 2 0.7628 0.0481 0.000 0.440 0.212 0.348
#> SRR659649 3 0.0000 0.9569 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.0188 0.8565 0.000 0.000 0.004 0.996
#> SRR1338257 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.0188 0.9570 0.000 0.000 0.996 0.004
#> SRR1350002 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1489757 3 0.0336 0.9564 0.000 0.000 0.992 0.008
#> SRR1414637 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1478113 4 0.1637 0.8637 0.000 0.060 0.000 0.940
#> SRR1322477 4 0.3764 0.7106 0.216 0.000 0.000 0.784
#> SRR1478789 3 0.2654 0.8570 0.000 0.108 0.888 0.004
#> SRR1414185 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1069141 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1376852 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.1398 0.9395 0.956 0.000 0.040 0.004
#> SRR1472012 1 0.0188 0.9757 0.996 0.000 0.004 0.000
#> SRR1340325 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1488790 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.1661 0.8964 0.000 0.944 0.052 0.004
#> SRR1089446 3 0.1389 0.9425 0.000 0.000 0.952 0.048
#> SRR1344445 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1412969 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1071668 3 0.0000 0.9569 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0188 0.9760 0.996 0.000 0.000 0.004
#> SRR1383283 2 0.0921 0.9238 0.000 0.972 0.000 0.028
#> SRR1350239 4 0.2345 0.8063 0.000 0.000 0.100 0.900
#> SRR1353878 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.1557 0.8645 0.000 0.056 0.000 0.944
#> SRR1098737 4 0.4855 0.3713 0.400 0.000 0.000 0.600
#> SRR1349409 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.0817 0.8523 0.000 0.000 0.024 0.976
#> SRR1407179 1 0.1743 0.9220 0.940 0.000 0.056 0.004
#> SRR1095913 2 0.0188 0.9401 0.000 0.996 0.004 0.000
#> SRR1403544 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0707 0.9641 0.980 0.000 0.000 0.020
#> SRR807971 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1436228 2 0.1209 0.9155 0.000 0.964 0.032 0.004
#> SRR1445218 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1485438 2 0.0779 0.9292 0.000 0.980 0.016 0.004
#> SRR1358143 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0336 0.9566 0.000 0.000 0.992 0.008
#> SRR1087007 3 0.0188 0.9571 0.000 0.000 0.996 0.004
#> SRR1086256 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1346734 4 0.1637 0.8637 0.000 0.060 0.000 0.940
#> SRR1414515 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1349320 4 0.1716 0.8624 0.000 0.064 0.000 0.936
#> SRR1317554 4 0.4522 0.5283 0.000 0.320 0.000 0.680
#> SRR1076022 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1339573 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1455878 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1387397 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.1118 0.9497 0.964 0.000 0.000 0.036
#> SRR1331488 4 0.1211 0.8527 0.040 0.000 0.000 0.960
#> SRR1499675 3 0.1022 0.9491 0.000 0.000 0.968 0.032
#> SRR1440467 3 0.1474 0.9408 0.000 0.000 0.948 0.052
#> SRR807995 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1476485 4 0.1211 0.8664 0.000 0.040 0.000 0.960
#> SRR1388214 1 0.3907 0.6959 0.768 0.000 0.000 0.232
#> SRR1456051 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.4632 0.5032 0.308 0.000 0.688 0.004
#> SRR1444083 1 0.3873 0.6912 0.772 0.000 0.000 0.228
#> SRR1313807 4 0.3320 0.8348 0.000 0.056 0.068 0.876
#> SRR1470751 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1403434 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1390540 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1325290 1 0.1302 0.9390 0.956 0.000 0.044 0.000
#> SRR1070689 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0336 0.9733 0.992 0.000 0.000 0.008
#> SRR1081184 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.5869 0.6294 0.096 0.196 0.704 0.004
#> SRR1321877 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR815711 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1433476 3 0.1557 0.9389 0.000 0.000 0.944 0.056
#> SRR1101883 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1433729 2 0.4018 0.6874 0.000 0.772 0.224 0.004
#> SRR1341877 1 0.0657 0.9663 0.984 0.000 0.012 0.004
#> SRR1090556 1 0.0336 0.9726 0.992 0.008 0.000 0.000
#> SRR1357389 3 0.0188 0.9570 0.000 0.000 0.996 0.004
#> SRR1404227 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1376830 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1080294 3 0.1661 0.9234 0.000 0.052 0.944 0.004
#> SRR1336314 4 0.2081 0.8534 0.000 0.084 0.000 0.916
#> SRR1102152 2 0.2216 0.8335 0.092 0.908 0.000 0.000
#> SRR1345244 3 0.0000 0.9569 0.000 0.000 1.000 0.000
#> SRR1478637 2 0.0336 0.9375 0.000 0.992 0.008 0.000
#> SRR1443776 3 0.0895 0.9455 0.000 0.020 0.976 0.004
#> SRR1120939 3 0.0188 0.9564 0.000 0.000 0.996 0.004
#> SRR1080117 3 0.0000 0.9569 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1091865 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1361072 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9786 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.0657 0.9510 0.000 0.012 0.984 0.004
#> SRR1389384 1 0.1302 0.9396 0.956 0.044 0.000 0.000
#> SRR1316096 2 0.0000 0.9423 0.000 1.000 0.000 0.000
#> SRR1408512 1 0.1118 0.9496 0.964 0.000 0.000 0.036
#> SRR1447547 4 0.4331 0.5603 0.000 0.000 0.288 0.712
#> SRR1354053 2 0.1716 0.8887 0.000 0.936 0.000 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.2471 0.8141 0.864 0.000 0.000 0.000 0.136
#> SRR1349562 1 0.0290 0.8699 0.992 0.000 0.000 0.000 0.008
#> SRR1353376 4 0.6398 0.5789 0.000 0.092 0.164 0.644 0.100
#> SRR1499040 1 0.5429 0.6449 0.676 0.096 0.000 0.012 0.216
#> SRR1322312 1 0.0162 0.8697 0.996 0.000 0.000 0.000 0.004
#> SRR1324412 3 0.3109 0.6511 0.000 0.000 0.800 0.000 0.200
#> SRR1100991 3 0.3398 0.6351 0.004 0.000 0.780 0.000 0.216
#> SRR1349479 3 0.1018 0.5806 0.000 0.000 0.968 0.016 0.016
#> SRR1431248 1 0.7084 0.0435 0.396 0.012 0.000 0.308 0.284
#> SRR1405054 1 0.1243 0.8627 0.960 0.000 0.004 0.008 0.028
#> SRR1312266 4 0.5787 0.5242 0.204 0.000 0.000 0.616 0.180
#> SRR1409790 3 0.3224 0.6280 0.000 0.000 0.824 0.016 0.160
#> SRR1352507 3 0.4584 0.5926 0.000 0.000 0.660 0.028 0.312
#> SRR1383763 1 0.0290 0.8708 0.992 0.000 0.000 0.000 0.008
#> SRR1468314 2 0.1410 0.8322 0.000 0.940 0.000 0.000 0.060
#> SRR1473674 2 0.0404 0.8306 0.000 0.988 0.000 0.000 0.012
#> SRR1390499 1 0.0404 0.8695 0.988 0.000 0.000 0.000 0.012
#> SRR821043 4 0.6102 0.5766 0.000 0.044 0.236 0.632 0.088
#> SRR1455653 2 0.4618 0.6619 0.000 0.724 0.000 0.208 0.068
#> SRR1335236 2 0.0162 0.8335 0.000 0.996 0.000 0.000 0.004
#> SRR1095383 4 0.4643 0.6555 0.000 0.024 0.064 0.768 0.144
#> SRR1479489 1 0.0162 0.8701 0.996 0.000 0.000 0.000 0.004
#> SRR1310433 2 0.1608 0.8304 0.000 0.928 0.000 0.000 0.072
#> SRR1073435 5 0.6768 0.2965 0.000 0.232 0.076 0.108 0.584
#> SRR659649 3 0.4644 0.3848 0.000 0.000 0.528 0.012 0.460
#> SRR1395999 1 0.0963 0.8687 0.964 0.000 0.000 0.000 0.036
#> SRR1105248 4 0.1809 0.7317 0.000 0.000 0.012 0.928 0.060
#> SRR1338257 1 0.4444 0.7191 0.748 0.000 0.000 0.072 0.180
#> SRR1499395 3 0.2813 0.6450 0.000 0.000 0.832 0.000 0.168
#> SRR1350002 2 0.0609 0.8287 0.000 0.980 0.000 0.000 0.020
#> SRR1489757 3 0.3177 0.6407 0.000 0.000 0.792 0.000 0.208
#> SRR1414637 2 0.3766 0.7290 0.000 0.728 0.000 0.004 0.268
#> SRR1478113 4 0.0955 0.7417 0.000 0.028 0.000 0.968 0.004
#> SRR1322477 4 0.5619 0.5385 0.208 0.000 0.000 0.636 0.156
#> SRR1478789 3 0.5484 0.3609 0.000 0.080 0.584 0.000 0.336
#> SRR1414185 3 0.4535 0.3426 0.000 0.000 0.752 0.140 0.108
#> SRR1069141 2 0.0000 0.8330 0.000 1.000 0.000 0.000 0.000
#> SRR1376852 1 0.1697 0.8516 0.932 0.000 0.000 0.008 0.060
#> SRR1323491 1 0.0290 0.8698 0.992 0.000 0.000 0.000 0.008
#> SRR1338103 1 0.3983 0.5549 0.660 0.000 0.000 0.000 0.340
#> SRR1472012 1 0.4054 0.7078 0.744 0.000 0.008 0.012 0.236
#> SRR1340325 1 0.1043 0.8648 0.960 0.000 0.000 0.000 0.040
#> SRR1087321 3 0.3480 0.6244 0.000 0.000 0.752 0.000 0.248
#> SRR1488790 1 0.0000 0.8699 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.5691 -0.3391 0.008 0.460 0.048 0.004 0.480
#> SRR1089446 3 0.0798 0.5857 0.000 0.000 0.976 0.016 0.008
#> SRR1344445 3 0.4356 0.5807 0.000 0.000 0.648 0.012 0.340
#> SRR1412969 3 0.1549 0.5770 0.000 0.000 0.944 0.016 0.040
#> SRR1071668 3 0.3586 0.6301 0.000 0.000 0.736 0.000 0.264
#> SRR1075804 1 0.1485 0.8628 0.948 0.000 0.000 0.032 0.020
#> SRR1383283 2 0.4636 0.6569 0.000 0.664 0.004 0.024 0.308
#> SRR1350239 4 0.4111 0.6617 0.000 0.000 0.120 0.788 0.092
#> SRR1353878 1 0.2471 0.8155 0.864 0.000 0.000 0.000 0.136
#> SRR1375721 1 0.0404 0.8691 0.988 0.000 0.000 0.000 0.012
#> SRR1083983 1 0.3318 0.7677 0.808 0.000 0.000 0.012 0.180
#> SRR1090095 1 0.0162 0.8697 0.996 0.000 0.000 0.000 0.004
#> SRR1414792 1 0.0290 0.8699 0.992 0.000 0.000 0.000 0.008
#> SRR1075102 4 0.0794 0.7419 0.000 0.028 0.000 0.972 0.000
#> SRR1098737 4 0.5901 0.3092 0.344 0.000 0.000 0.540 0.116
#> SRR1349409 1 0.0162 0.8697 0.996 0.000 0.000 0.000 0.004
#> SRR1413008 4 0.3754 0.6803 0.000 0.000 0.100 0.816 0.084
#> SRR1407179 1 0.4767 0.3267 0.560 0.000 0.020 0.000 0.420
#> SRR1095913 2 0.3039 0.7761 0.000 0.808 0.000 0.000 0.192
#> SRR1403544 1 0.0162 0.8697 0.996 0.000 0.000 0.000 0.004
#> SRR1490546 1 0.4437 0.7283 0.760 0.000 0.000 0.100 0.140
#> SRR807971 3 0.3990 0.6078 0.000 0.000 0.688 0.004 0.308
#> SRR1436228 2 0.4372 0.7302 0.072 0.756 0.000 0.000 0.172
#> SRR1445218 2 0.1851 0.8263 0.000 0.912 0.000 0.000 0.088
#> SRR1485438 2 0.2127 0.7986 0.000 0.892 0.000 0.000 0.108
#> SRR1358143 1 0.0290 0.8694 0.992 0.000 0.000 0.000 0.008
#> SRR1328760 1 0.3488 0.7735 0.808 0.000 0.000 0.024 0.168
#> SRR1380806 1 0.0162 0.8701 0.996 0.000 0.000 0.000 0.004
#> SRR1379426 3 0.5260 0.3733 0.000 0.000 0.604 0.064 0.332
#> SRR1087007 3 0.4016 0.5776 0.000 0.000 0.716 0.012 0.272
#> SRR1086256 2 0.3210 0.7664 0.000 0.788 0.000 0.000 0.212
#> SRR1346734 4 0.1830 0.7370 0.000 0.028 0.000 0.932 0.040
#> SRR1414515 1 0.1197 0.8573 0.952 0.000 0.000 0.000 0.048
#> SRR1082151 2 0.2852 0.7290 0.000 0.828 0.000 0.000 0.172
#> SRR1349320 4 0.1251 0.7420 0.000 0.036 0.000 0.956 0.008
#> SRR1317554 4 0.7185 -0.0384 0.000 0.352 0.024 0.404 0.220
#> SRR1076022 2 0.2813 0.7904 0.000 0.832 0.000 0.000 0.168
#> SRR1339573 3 0.3906 0.6165 0.000 0.000 0.704 0.004 0.292
#> SRR1455878 1 0.2130 0.8520 0.908 0.000 0.000 0.012 0.080
#> SRR1446203 3 0.3837 0.6081 0.000 0.000 0.692 0.000 0.308
#> SRR1387397 1 0.1478 0.8575 0.936 0.000 0.000 0.000 0.064
#> SRR1402590 1 0.0000 0.8699 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.4087 0.6885 0.756 0.000 0.000 0.208 0.036
#> SRR1331488 4 0.0510 0.7379 0.016 0.000 0.000 0.984 0.000
#> SRR1499675 3 0.2338 0.5083 0.000 0.000 0.884 0.004 0.112
#> SRR1440467 3 0.0912 0.5834 0.000 0.000 0.972 0.016 0.012
#> SRR807995 2 0.0510 0.8298 0.000 0.984 0.000 0.000 0.016
#> SRR1476485 4 0.1281 0.7396 0.000 0.012 0.000 0.956 0.032
#> SRR1388214 1 0.4164 0.7646 0.784 0.000 0.000 0.096 0.120
#> SRR1456051 1 0.0963 0.8654 0.964 0.000 0.000 0.000 0.036
#> SRR1473275 5 0.6002 -0.2131 0.112 0.000 0.436 0.000 0.452
#> SRR1444083 1 0.6161 0.4038 0.556 0.000 0.000 0.248 0.196
#> SRR1313807 4 0.5902 0.2033 0.000 0.044 0.028 0.476 0.452
#> SRR1470751 2 0.3039 0.7084 0.000 0.808 0.000 0.000 0.192
#> SRR1403434 3 0.1018 0.5806 0.000 0.000 0.968 0.016 0.016
#> SRR1390540 1 0.1270 0.8545 0.948 0.000 0.000 0.000 0.052
#> SRR1093861 2 0.0162 0.8340 0.000 0.996 0.000 0.000 0.004
#> SRR1325290 1 0.4822 0.5427 0.632 0.000 0.016 0.012 0.340
#> SRR1070689 1 0.0162 0.8701 0.996 0.000 0.000 0.000 0.004
#> SRR1384049 1 0.0671 0.8688 0.980 0.000 0.000 0.016 0.004
#> SRR1081184 1 0.0162 0.8701 0.996 0.000 0.000 0.000 0.004
#> SRR1324295 1 0.0162 0.8703 0.996 0.000 0.000 0.000 0.004
#> SRR1365313 5 0.7079 0.3855 0.044 0.196 0.240 0.000 0.520
#> SRR1321877 3 0.3983 0.5411 0.000 0.000 0.660 0.000 0.340
#> SRR815711 3 0.0912 0.5834 0.000 0.000 0.972 0.016 0.012
#> SRR1433476 3 0.2770 0.4799 0.000 0.000 0.880 0.044 0.076
#> SRR1101883 3 0.4127 0.6022 0.000 0.000 0.680 0.008 0.312
#> SRR1433729 2 0.5805 0.3644 0.000 0.520 0.040 0.028 0.412
#> SRR1341877 1 0.3661 0.6465 0.724 0.000 0.000 0.000 0.276
#> SRR1090556 1 0.5709 0.5935 0.636 0.008 0.000 0.116 0.240
#> SRR1357389 3 0.2929 0.6452 0.000 0.000 0.820 0.000 0.180
#> SRR1404227 5 0.4235 -0.0945 0.000 0.000 0.424 0.000 0.576
#> SRR1376830 1 0.0671 0.8697 0.980 0.000 0.000 0.004 0.016
#> SRR1500661 1 0.0162 0.8697 0.996 0.000 0.000 0.000 0.004
#> SRR1080294 3 0.8022 -0.3327 0.000 0.172 0.376 0.120 0.332
#> SRR1336314 4 0.4096 0.6408 0.000 0.200 0.000 0.760 0.040
#> SRR1102152 2 0.1768 0.7871 0.072 0.924 0.000 0.000 0.004
#> SRR1345244 3 0.3508 0.6234 0.000 0.000 0.748 0.000 0.252
#> SRR1478637 2 0.4695 0.5601 0.000 0.644 0.012 0.012 0.332
#> SRR1443776 3 0.4455 0.4474 0.000 0.008 0.588 0.000 0.404
#> SRR1120939 3 0.3796 0.6120 0.000 0.000 0.700 0.000 0.300
#> SRR1080117 3 0.3508 0.6206 0.000 0.000 0.748 0.000 0.252
#> SRR1102899 2 0.3210 0.7666 0.000 0.788 0.000 0.000 0.212
#> SRR1091865 1 0.6211 0.5173 0.572 0.156 0.000 0.008 0.264
#> SRR1361072 1 0.0404 0.8707 0.988 0.000 0.000 0.000 0.012
#> SRR1487890 1 0.0290 0.8698 0.992 0.000 0.000 0.000 0.008
#> SRR1349456 5 0.4961 -0.0276 0.000 0.028 0.448 0.000 0.524
#> SRR1389384 1 0.6918 0.2757 0.432 0.216 0.000 0.012 0.340
#> SRR1316096 2 0.0404 0.8347 0.000 0.988 0.000 0.000 0.012
#> SRR1408512 1 0.2570 0.8189 0.888 0.000 0.000 0.028 0.084
#> SRR1447547 3 0.5931 -0.2817 0.000 0.000 0.460 0.436 0.104
#> SRR1354053 2 0.1741 0.8295 0.000 0.936 0.000 0.040 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.2631 0.71665 0.820 0.000 0.000 0.000 0.180 0.000
#> SRR1349562 1 0.0260 0.81318 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1353376 4 0.4410 0.53218 0.000 0.032 0.000 0.724 0.036 0.208
#> SRR1499040 5 0.6874 0.37324 0.376 0.068 0.012 0.000 0.416 0.128
#> SRR1322312 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324412 3 0.3515 -0.00191 0.000 0.000 0.676 0.000 0.000 0.324
#> SRR1100991 3 0.3892 0.17083 0.020 0.000 0.752 0.000 0.020 0.208
#> SRR1349479 6 0.3907 0.53761 0.000 0.000 0.408 0.004 0.000 0.588
#> SRR1431248 5 0.6285 0.15107 0.172 0.012 0.044 0.192 0.580 0.000
#> SRR1405054 1 0.2006 0.77327 0.904 0.000 0.016 0.000 0.080 0.000
#> SRR1312266 4 0.5868 0.09131 0.168 0.000 0.000 0.428 0.400 0.004
#> SRR1409790 3 0.3707 -0.06063 0.000 0.000 0.680 0.000 0.008 0.312
#> SRR1352507 3 0.2813 0.42279 0.000 0.000 0.864 0.036 0.092 0.008
#> SRR1383763 1 0.0405 0.81369 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1468314 2 0.1124 0.78117 0.000 0.956 0.000 0.000 0.036 0.008
#> SRR1473674 2 0.1082 0.77403 0.000 0.956 0.000 0.000 0.040 0.004
#> SRR1390499 1 0.0713 0.81037 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR821043 4 0.6261 0.22672 0.000 0.184 0.000 0.420 0.020 0.376
#> SRR1455653 2 0.3926 0.68479 0.000 0.768 0.000 0.176 0.040 0.016
#> SRR1335236 2 0.0508 0.78031 0.000 0.984 0.000 0.000 0.012 0.004
#> SRR1095383 4 0.4237 0.60090 0.000 0.024 0.020 0.788 0.052 0.116
#> SRR1479489 1 0.0865 0.80724 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1310433 2 0.1370 0.77689 0.000 0.948 0.000 0.004 0.036 0.012
#> SRR1073435 3 0.7739 0.11586 0.000 0.144 0.424 0.036 0.140 0.256
#> SRR659649 3 0.4557 0.37700 0.000 0.000 0.660 0.000 0.268 0.072
#> SRR1395999 1 0.2250 0.78197 0.888 0.000 0.000 0.000 0.092 0.020
#> SRR1105248 4 0.0964 0.67241 0.000 0.004 0.000 0.968 0.012 0.016
#> SRR1338257 1 0.4680 0.17684 0.524 0.000 0.028 0.008 0.440 0.000
#> SRR1499395 3 0.5250 0.15039 0.000 0.000 0.540 0.000 0.108 0.352
#> SRR1350002 2 0.1285 0.77001 0.000 0.944 0.000 0.000 0.052 0.004
#> SRR1489757 3 0.3014 0.21754 0.000 0.000 0.804 0.000 0.012 0.184
#> SRR1414637 2 0.5509 0.43641 0.000 0.540 0.000 0.000 0.300 0.160
#> SRR1478113 4 0.1152 0.67555 0.000 0.004 0.000 0.952 0.044 0.000
#> SRR1322477 4 0.5535 0.32808 0.172 0.000 0.000 0.572 0.252 0.004
#> SRR1478789 3 0.5992 0.35169 0.000 0.024 0.564 0.004 0.160 0.248
#> SRR1414185 6 0.6444 0.33912 0.000 0.000 0.164 0.216 0.076 0.544
#> SRR1069141 2 0.0363 0.77967 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1376852 1 0.2340 0.71162 0.852 0.000 0.000 0.000 0.148 0.000
#> SRR1323491 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1338103 1 0.7190 -0.19063 0.420 0.000 0.096 0.004 0.196 0.284
#> SRR1472012 1 0.6277 -0.35438 0.412 0.000 0.024 0.000 0.392 0.172
#> SRR1340325 1 0.1958 0.77834 0.896 0.000 0.000 0.000 0.100 0.004
#> SRR1087321 3 0.4338 0.37789 0.000 0.000 0.716 0.004 0.072 0.208
#> SRR1488790 1 0.0000 0.81338 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.7735 -0.01948 0.000 0.144 0.208 0.012 0.340 0.296
#> SRR1089446 6 0.3789 0.53389 0.000 0.000 0.416 0.000 0.000 0.584
#> SRR1344445 3 0.2909 0.41941 0.000 0.000 0.836 0.000 0.136 0.028
#> SRR1412969 6 0.4436 0.47006 0.000 0.000 0.324 0.004 0.036 0.636
#> SRR1071668 3 0.2538 0.34009 0.000 0.000 0.860 0.000 0.016 0.124
#> SRR1075804 1 0.2277 0.78309 0.892 0.000 0.000 0.032 0.076 0.000
#> SRR1383283 2 0.6771 0.49071 0.000 0.524 0.040 0.032 0.156 0.248
#> SRR1350239 4 0.6412 0.45787 0.000 0.000 0.124 0.564 0.200 0.112
#> SRR1353878 1 0.2664 0.70784 0.816 0.000 0.000 0.000 0.184 0.000
#> SRR1375721 1 0.0405 0.81098 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1083983 1 0.5171 0.02903 0.560 0.000 0.000 0.000 0.336 0.104
#> SRR1090095 1 0.0000 0.81338 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0790 0.80850 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR1075102 4 0.1219 0.67382 0.000 0.004 0.000 0.948 0.048 0.000
#> SRR1098737 4 0.6755 -0.02364 0.308 0.000 0.032 0.400 0.256 0.004
#> SRR1349409 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1413008 4 0.5791 0.52910 0.000 0.000 0.088 0.632 0.188 0.092
#> SRR1407179 3 0.7432 -0.12601 0.232 0.000 0.404 0.004 0.132 0.228
#> SRR1095913 2 0.6365 0.51058 0.000 0.544 0.096 0.000 0.104 0.256
#> SRR1403544 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1490546 1 0.4009 0.53890 0.684 0.000 0.000 0.028 0.288 0.000
#> SRR807971 3 0.0363 0.44383 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1436228 2 0.6801 0.45351 0.040 0.524 0.020 0.004 0.204 0.208
#> SRR1445218 2 0.1857 0.77183 0.000 0.924 0.000 0.004 0.044 0.028
#> SRR1485438 2 0.3938 0.69083 0.000 0.784 0.008 0.004 0.136 0.068
#> SRR1358143 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1328760 1 0.3772 0.50264 0.672 0.000 0.000 0.004 0.320 0.004
#> SRR1380806 1 0.0000 0.81338 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.6796 0.23961 0.000 0.000 0.432 0.056 0.224 0.288
#> SRR1087007 3 0.5542 0.31535 0.000 0.000 0.560 0.020 0.096 0.324
#> SRR1086256 2 0.4952 0.63272 0.000 0.672 0.000 0.008 0.132 0.188
#> SRR1346734 4 0.0508 0.67460 0.000 0.004 0.000 0.984 0.000 0.012
#> SRR1414515 1 0.1643 0.76780 0.924 0.000 0.000 0.000 0.068 0.008
#> SRR1082151 2 0.3445 0.60233 0.000 0.732 0.000 0.000 0.260 0.008
#> SRR1349320 4 0.1082 0.67597 0.000 0.004 0.000 0.956 0.040 0.000
#> SRR1317554 4 0.6487 0.25987 0.000 0.240 0.000 0.528 0.072 0.160
#> SRR1076022 2 0.3997 0.69934 0.000 0.760 0.000 0.000 0.108 0.132
#> SRR1339573 3 0.2815 0.42494 0.000 0.000 0.848 0.000 0.032 0.120
#> SRR1455878 1 0.3893 0.64825 0.744 0.000 0.016 0.000 0.220 0.020
#> SRR1446203 3 0.1082 0.44804 0.000 0.000 0.956 0.000 0.004 0.040
#> SRR1387397 1 0.3966 0.67816 0.772 0.000 0.008 0.000 0.148 0.072
#> SRR1402590 1 0.0146 0.81326 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1317532 1 0.5324 0.29092 0.588 0.000 0.000 0.280 0.128 0.004
#> SRR1331488 4 0.1003 0.67598 0.004 0.000 0.000 0.964 0.028 0.004
#> SRR1499675 6 0.4893 0.34690 0.004 0.000 0.224 0.000 0.112 0.660
#> SRR1440467 6 0.3782 0.53560 0.000 0.000 0.412 0.000 0.000 0.588
#> SRR807995 2 0.1152 0.77333 0.000 0.952 0.000 0.000 0.044 0.004
#> SRR1476485 4 0.0291 0.67559 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1388214 1 0.3770 0.65426 0.752 0.000 0.000 0.032 0.212 0.004
#> SRR1456051 1 0.1863 0.77920 0.896 0.000 0.000 0.000 0.104 0.000
#> SRR1473275 3 0.6185 0.37375 0.056 0.000 0.584 0.004 0.152 0.204
#> SRR1444083 1 0.5746 -0.05117 0.448 0.000 0.016 0.108 0.428 0.000
#> SRR1313807 4 0.7851 0.17889 0.000 0.028 0.224 0.384 0.128 0.236
#> SRR1470751 2 0.3575 0.57472 0.000 0.708 0.000 0.000 0.284 0.008
#> SRR1403434 6 0.3782 0.53560 0.000 0.000 0.412 0.000 0.000 0.588
#> SRR1390540 1 0.1858 0.75629 0.912 0.000 0.000 0.000 0.076 0.012
#> SRR1093861 2 0.0291 0.78128 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1325290 5 0.7187 0.36858 0.284 0.000 0.100 0.000 0.396 0.220
#> SRR1070689 1 0.0000 0.81338 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.1606 0.78238 0.932 0.000 0.000 0.056 0.004 0.008
#> SRR1081184 1 0.0000 0.81338 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0146 0.81326 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1365313 6 0.7693 -0.21281 0.032 0.056 0.300 0.004 0.292 0.316
#> SRR1321877 3 0.5224 0.37047 0.000 0.000 0.608 0.000 0.164 0.228
#> SRR815711 6 0.3789 0.53389 0.000 0.000 0.416 0.000 0.000 0.584
#> SRR1433476 6 0.4835 0.51304 0.000 0.000 0.336 0.072 0.000 0.592
#> SRR1101883 3 0.1921 0.43978 0.000 0.000 0.916 0.000 0.052 0.032
#> SRR1433729 6 0.8440 -0.19259 0.000 0.248 0.260 0.060 0.160 0.272
#> SRR1341877 1 0.6971 0.06658 0.536 0.000 0.080 0.028 0.196 0.160
#> SRR1090556 5 0.7690 0.20736 0.348 0.028 0.128 0.056 0.404 0.036
#> SRR1357389 3 0.3547 -0.05161 0.000 0.000 0.668 0.000 0.000 0.332
#> SRR1404227 3 0.5638 0.26776 0.000 0.000 0.504 0.004 0.140 0.352
#> SRR1376830 1 0.1141 0.80218 0.948 0.000 0.000 0.000 0.052 0.000
#> SRR1500661 1 0.0146 0.81292 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1080294 3 0.8347 0.02349 0.000 0.244 0.332 0.124 0.072 0.228
#> SRR1336314 2 0.5514 0.02877 0.000 0.464 0.000 0.420 0.112 0.004
#> SRR1102152 2 0.2122 0.72254 0.076 0.900 0.000 0.000 0.024 0.000
#> SRR1345244 3 0.4582 0.36624 0.000 0.000 0.684 0.000 0.100 0.216
#> SRR1478637 5 0.6757 0.15442 0.004 0.268 0.052 0.000 0.468 0.208
#> SRR1443776 3 0.5597 0.37103 0.000 0.000 0.568 0.004 0.224 0.204
#> SRR1120939 3 0.1967 0.42216 0.000 0.000 0.904 0.000 0.012 0.084
#> SRR1080117 3 0.4768 0.35224 0.000 0.000 0.668 0.008 0.080 0.244
#> SRR1102899 2 0.4321 0.70001 0.000 0.760 0.004 0.016 0.080 0.140
#> SRR1091865 5 0.6033 0.30732 0.352 0.212 0.000 0.000 0.432 0.004
#> SRR1361072 1 0.0777 0.81166 0.972 0.000 0.000 0.000 0.024 0.004
#> SRR1487890 1 0.0260 0.81193 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1349456 3 0.6464 0.24245 0.000 0.012 0.388 0.004 0.256 0.340
#> SRR1389384 5 0.6997 0.43673 0.168 0.216 0.008 0.004 0.512 0.092
#> SRR1316096 2 0.0291 0.78098 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1408512 1 0.3953 0.65817 0.800 0.000 0.000 0.052 0.048 0.100
#> SRR1447547 6 0.7240 0.21524 0.000 0.000 0.176 0.240 0.152 0.432
#> SRR1354053 2 0.1503 0.77997 0.000 0.944 0.000 0.016 0.032 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.313 0.701 0.853 0.4232 0.515 0.515
#> 3 3 0.334 0.543 0.783 0.3208 0.864 0.749
#> 4 4 0.474 0.663 0.820 0.1382 0.816 0.621
#> 5 5 0.518 0.623 0.794 0.0775 0.963 0.900
#> 6 6 0.563 0.488 0.734 0.0723 0.942 0.831
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.8578 1.000 0.000
#> SRR1349562 1 0.0000 0.8578 1.000 0.000
#> SRR1353376 2 0.8207 0.6617 0.256 0.744
#> SRR1499040 1 0.5059 0.8144 0.888 0.112
#> SRR1322312 1 0.0000 0.8578 1.000 0.000
#> SRR1324412 1 0.8081 0.6129 0.752 0.248
#> SRR1100991 1 0.8081 0.6129 0.752 0.248
#> SRR1349479 2 0.8207 0.6617 0.256 0.744
#> SRR1431248 1 0.6801 0.7529 0.820 0.180
#> SRR1405054 1 0.7674 0.6590 0.776 0.224
#> SRR1312266 1 0.0000 0.8578 1.000 0.000
#> SRR1409790 1 0.8081 0.6129 0.752 0.248
#> SRR1352507 1 0.8081 0.6129 0.752 0.248
#> SRR1383763 1 0.0000 0.8578 1.000 0.000
#> SRR1468314 2 0.3733 0.7094 0.072 0.928
#> SRR1473674 2 0.0376 0.7131 0.004 0.996
#> SRR1390499 1 0.0000 0.8578 1.000 0.000
#> SRR821043 2 0.0000 0.7110 0.000 1.000
#> SRR1455653 2 0.0000 0.7110 0.000 1.000
#> SRR1335236 2 0.0376 0.7131 0.004 0.996
#> SRR1095383 2 0.0000 0.7110 0.000 1.000
#> SRR1479489 1 0.1843 0.8509 0.972 0.028
#> SRR1310433 2 0.0376 0.7131 0.004 0.996
#> SRR1073435 2 0.9358 0.5890 0.352 0.648
#> SRR659649 2 0.9933 0.4173 0.452 0.548
#> SRR1395999 1 0.1414 0.8549 0.980 0.020
#> SRR1105248 2 0.9635 0.5317 0.388 0.612
#> SRR1338257 1 0.0376 0.8579 0.996 0.004
#> SRR1499395 2 0.9988 0.3254 0.480 0.520
#> SRR1350002 2 0.0376 0.7131 0.004 0.996
#> SRR1489757 1 0.8081 0.6129 0.752 0.248
#> SRR1414637 1 0.6801 0.7504 0.820 0.180
#> SRR1478113 2 0.3431 0.7093 0.064 0.936
#> SRR1322477 1 0.5294 0.8129 0.880 0.120
#> SRR1478789 2 0.9933 0.4173 0.452 0.548
#> SRR1414185 2 0.9933 0.4173 0.452 0.548
#> SRR1069141 2 0.0376 0.7131 0.004 0.996
#> SRR1376852 1 0.2043 0.8507 0.968 0.032
#> SRR1323491 1 0.0000 0.8578 1.000 0.000
#> SRR1338103 1 0.6973 0.7377 0.812 0.188
#> SRR1472012 1 0.5629 0.8016 0.868 0.132
#> SRR1340325 1 0.0376 0.8579 0.996 0.004
#> SRR1087321 2 0.9933 0.4173 0.452 0.548
#> SRR1488790 1 0.0000 0.8578 1.000 0.000
#> SRR1334866 1 0.6438 0.7692 0.836 0.164
#> SRR1089446 1 0.7674 0.6690 0.776 0.224
#> SRR1344445 1 0.8861 0.5016 0.696 0.304
#> SRR1412969 2 0.9933 0.4173 0.452 0.548
#> SRR1071668 1 0.8016 0.6231 0.756 0.244
#> SRR1075804 1 0.1414 0.8563 0.980 0.020
#> SRR1383283 2 0.9358 0.5890 0.352 0.648
#> SRR1350239 2 0.9686 0.5178 0.396 0.604
#> SRR1353878 1 0.0000 0.8578 1.000 0.000
#> SRR1375721 1 0.0000 0.8578 1.000 0.000
#> SRR1083983 1 0.5737 0.7975 0.864 0.136
#> SRR1090095 1 0.0000 0.8578 1.000 0.000
#> SRR1414792 1 0.0000 0.8578 1.000 0.000
#> SRR1075102 2 0.3431 0.7093 0.064 0.936
#> SRR1098737 1 0.1414 0.8563 0.980 0.020
#> SRR1349409 1 0.0000 0.8578 1.000 0.000
#> SRR1413008 2 0.9686 0.5178 0.396 0.604
#> SRR1407179 1 0.8443 0.6018 0.728 0.272
#> SRR1095913 2 0.9358 0.5880 0.352 0.648
#> SRR1403544 1 0.0000 0.8578 1.000 0.000
#> SRR1490546 1 0.0000 0.8578 1.000 0.000
#> SRR807971 1 0.8861 0.5016 0.696 0.304
#> SRR1436228 1 0.8267 0.6337 0.740 0.260
#> SRR1445218 2 0.0376 0.7131 0.004 0.996
#> SRR1485438 2 0.0376 0.7131 0.004 0.996
#> SRR1358143 1 0.0000 0.8578 1.000 0.000
#> SRR1328760 1 0.0000 0.8578 1.000 0.000
#> SRR1380806 1 0.0000 0.8578 1.000 0.000
#> SRR1379426 2 0.9933 0.4173 0.452 0.548
#> SRR1087007 2 0.9933 0.4173 0.452 0.548
#> SRR1086256 1 0.6887 0.7453 0.816 0.184
#> SRR1346734 2 0.0000 0.7110 0.000 1.000
#> SRR1414515 1 0.0000 0.8578 1.000 0.000
#> SRR1082151 1 0.4815 0.8167 0.896 0.104
#> SRR1349320 2 0.3431 0.7093 0.064 0.936
#> SRR1317554 2 0.0000 0.7110 0.000 1.000
#> SRR1076022 2 0.0376 0.7131 0.004 0.996
#> SRR1339573 1 0.9944 -0.0781 0.544 0.456
#> SRR1455878 1 0.1843 0.8533 0.972 0.028
#> SRR1446203 2 0.9933 0.4173 0.452 0.548
#> SRR1387397 1 0.5737 0.8015 0.864 0.136
#> SRR1402590 1 0.0000 0.8578 1.000 0.000
#> SRR1317532 1 0.1184 0.8568 0.984 0.016
#> SRR1331488 1 0.0938 0.8569 0.988 0.012
#> SRR1499675 1 0.6973 0.7377 0.812 0.188
#> SRR1440467 2 0.8327 0.6576 0.264 0.736
#> SRR807995 2 0.0376 0.7131 0.004 0.996
#> SRR1476485 2 0.0000 0.7110 0.000 1.000
#> SRR1388214 1 0.0672 0.8577 0.992 0.008
#> SRR1456051 1 0.0000 0.8578 1.000 0.000
#> SRR1473275 1 0.9732 0.1631 0.596 0.404
#> SRR1444083 1 0.0376 0.8579 0.996 0.004
#> SRR1313807 2 0.9248 0.6007 0.340 0.660
#> SRR1470751 1 0.4815 0.8167 0.896 0.104
#> SRR1403434 2 0.8763 0.6371 0.296 0.704
#> SRR1390540 1 0.0000 0.8578 1.000 0.000
#> SRR1093861 2 0.0376 0.7131 0.004 0.996
#> SRR1325290 1 0.5629 0.8016 0.868 0.132
#> SRR1070689 1 0.0000 0.8578 1.000 0.000
#> SRR1384049 1 0.0000 0.8578 1.000 0.000
#> SRR1081184 1 0.0000 0.8578 1.000 0.000
#> SRR1324295 1 0.0000 0.8578 1.000 0.000
#> SRR1365313 1 0.8861 0.5270 0.696 0.304
#> SRR1321877 2 0.9933 0.4173 0.452 0.548
#> SRR815711 1 0.7883 0.6384 0.764 0.236
#> SRR1433476 2 0.8207 0.6617 0.256 0.744
#> SRR1101883 1 0.8861 0.5016 0.696 0.304
#> SRR1433729 2 0.8386 0.6519 0.268 0.732
#> SRR1341877 1 0.6973 0.7377 0.812 0.188
#> SRR1090556 1 0.7299 0.7191 0.796 0.204
#> SRR1357389 1 0.8207 0.5980 0.744 0.256
#> SRR1404227 2 0.9358 0.5880 0.352 0.648
#> SRR1376830 1 0.0000 0.8578 1.000 0.000
#> SRR1500661 1 0.0000 0.8578 1.000 0.000
#> SRR1080294 2 0.0000 0.7110 0.000 1.000
#> SRR1336314 2 0.0000 0.7110 0.000 1.000
#> SRR1102152 1 0.0938 0.8568 0.988 0.012
#> SRR1345244 2 0.9933 0.4173 0.452 0.548
#> SRR1478637 1 0.6148 0.7792 0.848 0.152
#> SRR1443776 2 0.9933 0.4173 0.452 0.548
#> SRR1120939 2 0.9933 0.4173 0.452 0.548
#> SRR1080117 2 0.9933 0.4173 0.452 0.548
#> SRR1102899 2 0.0376 0.7131 0.004 0.996
#> SRR1091865 1 0.1184 0.8566 0.984 0.016
#> SRR1361072 1 0.0000 0.8578 1.000 0.000
#> SRR1487890 1 0.0000 0.8578 1.000 0.000
#> SRR1349456 2 0.9358 0.5880 0.352 0.648
#> SRR1389384 1 0.4815 0.8167 0.896 0.104
#> SRR1316096 2 0.0376 0.7131 0.004 0.996
#> SRR1408512 1 0.4431 0.8289 0.908 0.092
#> SRR1447547 2 0.9635 0.5301 0.388 0.612
#> SRR1354053 2 0.0000 0.7110 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1353376 3 0.4555 0.52187 0.000 0.200 0.800
#> SRR1499040 1 0.5497 0.64144 0.708 0.000 0.292
#> SRR1322312 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1324412 1 0.6295 0.18450 0.528 0.000 0.472
#> SRR1100991 1 0.6295 0.18450 0.528 0.000 0.472
#> SRR1349479 3 0.4504 0.52246 0.000 0.196 0.804
#> SRR1431248 1 0.6282 0.51859 0.612 0.004 0.384
#> SRR1405054 1 0.6215 0.30627 0.572 0.000 0.428
#> SRR1312266 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1409790 1 0.6295 0.18450 0.528 0.000 0.472
#> SRR1352507 1 0.6295 0.18450 0.528 0.000 0.472
#> SRR1383763 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1468314 3 0.6252 -0.21174 0.000 0.444 0.556
#> SRR1473674 3 0.6045 -0.15241 0.000 0.380 0.620
#> SRR1390499 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR821043 2 0.0424 0.83249 0.000 0.992 0.008
#> SRR1455653 2 0.2165 0.82405 0.000 0.936 0.064
#> SRR1335236 3 0.6244 -0.26667 0.000 0.440 0.560
#> SRR1095383 2 0.5216 0.63219 0.000 0.740 0.260
#> SRR1479489 1 0.2711 0.76509 0.912 0.000 0.088
#> SRR1310433 3 0.6252 -0.27564 0.000 0.444 0.556
#> SRR1073435 3 0.5663 0.63643 0.096 0.096 0.808
#> SRR659649 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1395999 1 0.1860 0.77023 0.948 0.000 0.052
#> SRR1105248 3 0.7653 0.58845 0.140 0.176 0.684
#> SRR1338257 1 0.2537 0.76838 0.920 0.000 0.080
#> SRR1499395 3 0.4796 0.59526 0.220 0.000 0.780
#> SRR1350002 3 0.5882 -0.09455 0.000 0.348 0.652
#> SRR1489757 1 0.6295 0.18450 0.528 0.000 0.472
#> SRR1414637 1 0.6434 0.52573 0.612 0.008 0.380
#> SRR1478113 2 0.4326 0.75055 0.012 0.844 0.144
#> SRR1322477 1 0.5178 0.67263 0.744 0.000 0.256
#> SRR1478789 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1414185 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1069141 3 0.6244 -0.26667 0.000 0.440 0.560
#> SRR1376852 1 0.3412 0.75385 0.876 0.000 0.124
#> SRR1323491 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1338103 1 0.6026 0.52847 0.624 0.000 0.376
#> SRR1472012 1 0.5835 0.59127 0.660 0.000 0.340
#> SRR1340325 1 0.2796 0.76502 0.908 0.000 0.092
#> SRR1087321 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1488790 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1334866 1 0.6209 0.55188 0.628 0.004 0.368
#> SRR1089446 1 0.6641 0.27158 0.544 0.008 0.448
#> SRR1344445 3 0.6280 0.00686 0.460 0.000 0.540
#> SRR1412969 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1071668 1 0.6291 0.20385 0.532 0.000 0.468
#> SRR1075804 1 0.2711 0.76759 0.912 0.000 0.088
#> SRR1383283 3 0.5663 0.63643 0.096 0.096 0.808
#> SRR1350239 3 0.7917 0.57625 0.152 0.184 0.664
#> SRR1353878 1 0.1411 0.77501 0.964 0.000 0.036
#> SRR1375721 1 0.0237 0.77514 0.996 0.000 0.004
#> SRR1083983 1 0.5859 0.58459 0.656 0.000 0.344
#> SRR1090095 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1075102 2 0.4326 0.75055 0.012 0.844 0.144
#> SRR1098737 1 0.2711 0.76759 0.912 0.000 0.088
#> SRR1349409 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1413008 3 0.7917 0.57625 0.152 0.184 0.664
#> SRR1407179 1 0.6307 0.28390 0.512 0.000 0.488
#> SRR1095913 3 0.5965 0.63936 0.108 0.100 0.792
#> SRR1403544 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR807971 3 0.6280 0.00686 0.460 0.000 0.540
#> SRR1436228 1 0.6505 0.33177 0.528 0.004 0.468
#> SRR1445218 3 0.6252 -0.27564 0.000 0.444 0.556
#> SRR1485438 3 0.5882 -0.09455 0.000 0.348 0.652
#> SRR1358143 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1328760 1 0.1411 0.77501 0.964 0.000 0.036
#> SRR1380806 1 0.0237 0.77419 0.996 0.000 0.004
#> SRR1379426 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1087007 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1086256 1 0.6451 0.51816 0.608 0.008 0.384
#> SRR1346734 2 0.0424 0.83249 0.000 0.992 0.008
#> SRR1414515 1 0.0237 0.77514 0.996 0.000 0.004
#> SRR1082151 1 0.5397 0.65266 0.720 0.000 0.280
#> SRR1349320 2 0.4326 0.75055 0.012 0.844 0.144
#> SRR1317554 2 0.0592 0.83280 0.000 0.988 0.012
#> SRR1076022 3 0.6291 -0.28874 0.000 0.468 0.532
#> SRR1339573 3 0.5465 0.47585 0.288 0.000 0.712
#> SRR1455878 1 0.3482 0.75364 0.872 0.000 0.128
#> SRR1446203 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1387397 1 0.5650 0.62616 0.688 0.000 0.312
#> SRR1402590 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1317532 1 0.2796 0.76718 0.908 0.000 0.092
#> SRR1331488 1 0.0661 0.77184 0.988 0.008 0.004
#> SRR1499675 1 0.6026 0.52847 0.624 0.000 0.376
#> SRR1440467 3 0.4399 0.52939 0.000 0.188 0.812
#> SRR807995 3 0.5926 -0.11025 0.000 0.356 0.644
#> SRR1476485 2 0.0424 0.83249 0.000 0.992 0.008
#> SRR1388214 1 0.3116 0.75957 0.892 0.000 0.108
#> SRR1456051 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1473275 3 0.5926 0.33456 0.356 0.000 0.644
#> SRR1444083 1 0.2537 0.76827 0.920 0.000 0.080
#> SRR1313807 3 0.5961 0.63022 0.096 0.112 0.792
#> SRR1470751 1 0.5397 0.65266 0.720 0.000 0.280
#> SRR1403434 3 0.5119 0.57712 0.032 0.152 0.816
#> SRR1390540 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1093861 3 0.6111 -0.18111 0.000 0.396 0.604
#> SRR1325290 1 0.5835 0.59127 0.660 0.000 0.340
#> SRR1070689 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1365313 3 0.6516 -0.20155 0.480 0.004 0.516
#> SRR1321877 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR815711 1 0.6280 0.22983 0.540 0.000 0.460
#> SRR1433476 3 0.4555 0.52187 0.000 0.200 0.800
#> SRR1101883 3 0.6280 0.00686 0.460 0.000 0.540
#> SRR1433729 3 0.7295 0.51157 0.072 0.252 0.676
#> SRR1341877 1 0.6026 0.52847 0.624 0.000 0.376
#> SRR1090556 1 0.6154 0.48233 0.592 0.000 0.408
#> SRR1357389 1 0.6302 0.16031 0.520 0.000 0.480
#> SRR1404227 3 0.5965 0.63936 0.108 0.100 0.792
#> SRR1376830 1 0.0237 0.77514 0.996 0.000 0.004
#> SRR1500661 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1080294 2 0.5216 0.63219 0.000 0.740 0.260
#> SRR1336314 2 0.0424 0.83249 0.000 0.992 0.008
#> SRR1102152 1 0.3340 0.75487 0.880 0.000 0.120
#> SRR1345244 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1478637 1 0.6169 0.55908 0.636 0.004 0.360
#> SRR1443776 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1120939 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1080117 3 0.4399 0.63796 0.188 0.000 0.812
#> SRR1102899 2 0.6260 0.30214 0.000 0.552 0.448
#> SRR1091865 1 0.2625 0.76866 0.916 0.000 0.084
#> SRR1361072 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.77468 1.000 0.000 0.000
#> SRR1349456 3 0.5965 0.63936 0.108 0.100 0.792
#> SRR1389384 1 0.5397 0.65266 0.720 0.000 0.280
#> SRR1316096 3 0.6140 -0.19650 0.000 0.404 0.596
#> SRR1408512 1 0.4887 0.69698 0.772 0.000 0.228
#> SRR1447547 3 0.7909 0.57444 0.148 0.188 0.664
#> SRR1354053 2 0.2261 0.82312 0.000 0.932 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1349562 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1353376 3 0.4079 0.6040 0.000 0.020 0.800 0.180
#> SRR1499040 1 0.6323 0.4838 0.592 0.012 0.348 0.048
#> SRR1322312 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1324412 3 0.4761 0.4804 0.372 0.000 0.628 0.000
#> SRR1100991 3 0.4761 0.4804 0.372 0.000 0.628 0.000
#> SRR1349479 3 0.3881 0.6084 0.000 0.016 0.812 0.172
#> SRR1431248 1 0.6519 0.3592 0.548 0.012 0.388 0.052
#> SRR1405054 3 0.4961 0.2974 0.448 0.000 0.552 0.000
#> SRR1312266 1 0.0000 0.8119 1.000 0.000 0.000 0.000
#> SRR1409790 3 0.4761 0.4804 0.372 0.000 0.628 0.000
#> SRR1352507 3 0.4761 0.4804 0.372 0.000 0.628 0.000
#> SRR1383763 1 0.3025 0.7500 0.896 0.044 0.056 0.004
#> SRR1468314 2 0.5452 0.6641 0.000 0.736 0.156 0.108
#> SRR1473674 2 0.2730 0.8220 0.000 0.896 0.088 0.016
#> SRR1390499 1 0.0188 0.8119 0.996 0.004 0.000 0.000
#> SRR821043 4 0.2868 0.8276 0.000 0.136 0.000 0.864
#> SRR1455653 4 0.4877 0.6274 0.000 0.328 0.008 0.664
#> SRR1335236 2 0.2586 0.8265 0.000 0.912 0.048 0.040
#> SRR1095383 2 0.5881 0.1084 0.000 0.544 0.036 0.420
#> SRR1479489 1 0.2281 0.7882 0.904 0.000 0.096 0.000
#> SRR1310433 2 0.2675 0.8256 0.000 0.908 0.048 0.044
#> SRR1073435 3 0.4990 0.6446 0.032 0.100 0.804 0.064
#> SRR659649 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1395999 1 0.1722 0.8045 0.944 0.000 0.048 0.008
#> SRR1105248 3 0.5166 0.6628 0.080 0.004 0.764 0.152
#> SRR1338257 1 0.2281 0.7878 0.904 0.000 0.096 0.000
#> SRR1499395 3 0.1978 0.7372 0.068 0.004 0.928 0.000
#> SRR1350002 2 0.3525 0.8015 0.000 0.860 0.100 0.040
#> SRR1489757 3 0.4761 0.4804 0.372 0.000 0.628 0.000
#> SRR1414637 1 0.7105 0.4315 0.556 0.020 0.336 0.088
#> SRR1478113 4 0.2352 0.7849 0.012 0.016 0.044 0.928
#> SRR1322477 1 0.5281 0.6411 0.716 0.008 0.244 0.032
#> SRR1478789 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1414185 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1069141 2 0.2586 0.8265 0.000 0.912 0.048 0.040
#> SRR1376852 1 0.3142 0.7703 0.860 0.000 0.132 0.008
#> SRR1323491 1 0.0188 0.8120 0.996 0.000 0.000 0.004
#> SRR1338103 1 0.5482 0.3657 0.572 0.004 0.412 0.012
#> SRR1472012 1 0.6235 0.4623 0.588 0.008 0.356 0.048
#> SRR1340325 1 0.2530 0.7793 0.888 0.000 0.112 0.000
#> SRR1087321 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1488790 1 0.0188 0.8120 0.996 0.000 0.000 0.004
#> SRR1334866 1 0.6838 0.4564 0.572 0.016 0.336 0.076
#> SRR1089446 3 0.5244 0.3171 0.436 0.000 0.556 0.008
#> SRR1344445 3 0.4431 0.5751 0.304 0.000 0.696 0.000
#> SRR1412969 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1071668 3 0.4877 0.4090 0.408 0.000 0.592 0.000
#> SRR1075804 1 0.2773 0.7836 0.880 0.000 0.116 0.004
#> SRR1383283 3 0.4990 0.6446 0.032 0.100 0.804 0.064
#> SRR1350239 3 0.5477 0.6437 0.092 0.000 0.728 0.180
#> SRR1353878 1 0.1302 0.8068 0.956 0.000 0.044 0.000
#> SRR1375721 1 0.0564 0.8125 0.988 0.004 0.004 0.004
#> SRR1083983 1 0.6158 0.4610 0.592 0.004 0.352 0.052
#> SRR1090095 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1414792 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1075102 4 0.2352 0.7849 0.012 0.016 0.044 0.928
#> SRR1098737 1 0.2773 0.7836 0.880 0.000 0.116 0.004
#> SRR1349409 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1413008 3 0.5477 0.6437 0.092 0.000 0.728 0.180
#> SRR1407179 3 0.5778 0.0360 0.444 0.008 0.532 0.016
#> SRR1095913 3 0.4914 0.6390 0.032 0.116 0.804 0.048
#> SRR1403544 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1490546 1 0.0000 0.8119 1.000 0.000 0.000 0.000
#> SRR807971 3 0.4431 0.5751 0.304 0.000 0.696 0.000
#> SRR1436228 3 0.5909 -0.0281 0.460 0.012 0.512 0.016
#> SRR1445218 2 0.2675 0.8256 0.000 0.908 0.048 0.044
#> SRR1485438 2 0.3525 0.8015 0.000 0.860 0.100 0.040
#> SRR1358143 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1328760 1 0.1302 0.8068 0.956 0.000 0.044 0.000
#> SRR1380806 1 0.0712 0.8117 0.984 0.004 0.008 0.004
#> SRR1379426 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1087007 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1086256 1 0.7119 0.4221 0.552 0.020 0.340 0.088
#> SRR1346734 4 0.2216 0.8385 0.000 0.092 0.000 0.908
#> SRR1414515 1 0.0564 0.8125 0.988 0.004 0.004 0.004
#> SRR1082151 1 0.5837 0.5929 0.668 0.008 0.276 0.048
#> SRR1349320 4 0.2352 0.7849 0.012 0.016 0.044 0.928
#> SRR1317554 4 0.3688 0.7875 0.000 0.208 0.000 0.792
#> SRR1076022 2 0.3168 0.8189 0.000 0.884 0.060 0.056
#> SRR1339573 3 0.3052 0.7205 0.136 0.004 0.860 0.000
#> SRR1455878 1 0.3249 0.7657 0.852 0.000 0.140 0.008
#> SRR1446203 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1387397 1 0.5378 0.5038 0.632 0.004 0.348 0.016
#> SRR1402590 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1317532 1 0.2654 0.7870 0.888 0.000 0.108 0.004
#> SRR1331488 1 0.0927 0.8113 0.976 0.000 0.016 0.008
#> SRR1499675 1 0.5375 0.3593 0.572 0.004 0.416 0.008
#> SRR1440467 3 0.3790 0.6166 0.000 0.016 0.820 0.164
#> SRR807995 2 0.3399 0.8080 0.000 0.868 0.092 0.040
#> SRR1476485 4 0.2216 0.8385 0.000 0.092 0.000 0.908
#> SRR1388214 1 0.3074 0.7517 0.848 0.000 0.152 0.000
#> SRR1456051 1 0.0188 0.8119 0.996 0.004 0.000 0.000
#> SRR1473275 3 0.3791 0.6778 0.200 0.000 0.796 0.004
#> SRR1444083 1 0.2281 0.7876 0.904 0.000 0.096 0.000
#> SRR1313807 3 0.5147 0.6318 0.032 0.116 0.792 0.060
#> SRR1470751 1 0.5837 0.5929 0.668 0.008 0.276 0.048
#> SRR1403434 3 0.3271 0.6477 0.000 0.012 0.856 0.132
#> SRR1390540 1 0.0188 0.8120 0.996 0.000 0.000 0.004
#> SRR1093861 2 0.2266 0.8267 0.000 0.912 0.084 0.004
#> SRR1325290 1 0.6291 0.4653 0.588 0.008 0.352 0.052
#> SRR1070689 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1384049 1 0.3025 0.7500 0.896 0.044 0.056 0.004
#> SRR1081184 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1324295 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1365313 3 0.5614 0.1737 0.412 0.012 0.568 0.008
#> SRR1321877 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR815711 3 0.4898 0.3879 0.416 0.000 0.584 0.000
#> SRR1433476 3 0.4079 0.6040 0.000 0.020 0.800 0.180
#> SRR1101883 3 0.4431 0.5751 0.304 0.000 0.696 0.000
#> SRR1433729 3 0.6396 0.4682 0.016 0.228 0.668 0.088
#> SRR1341877 1 0.5482 0.3657 0.572 0.004 0.412 0.012
#> SRR1090556 1 0.5773 0.2774 0.536 0.008 0.440 0.016
#> SRR1357389 3 0.4730 0.4913 0.364 0.000 0.636 0.000
#> SRR1404227 3 0.4914 0.6390 0.032 0.116 0.804 0.048
#> SRR1376830 1 0.0336 0.8120 0.992 0.000 0.008 0.000
#> SRR1500661 1 0.0376 0.8115 0.992 0.004 0.000 0.004
#> SRR1080294 2 0.5881 0.1084 0.000 0.544 0.036 0.420
#> SRR1336314 4 0.2216 0.8385 0.000 0.092 0.000 0.908
#> SRR1102152 1 0.3266 0.7397 0.832 0.000 0.168 0.000
#> SRR1345244 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1478637 1 0.6674 0.3167 0.520 0.016 0.412 0.052
#> SRR1443776 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1120939 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1080117 3 0.1305 0.7364 0.036 0.004 0.960 0.000
#> SRR1102899 2 0.4462 0.7430 0.000 0.804 0.064 0.132
#> SRR1091865 1 0.2777 0.7829 0.888 0.004 0.104 0.004
#> SRR1361072 1 0.0000 0.8119 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0376 0.8113 0.992 0.004 0.000 0.004
#> SRR1349456 3 0.4914 0.6390 0.032 0.116 0.804 0.048
#> SRR1389384 1 0.5837 0.5929 0.668 0.008 0.276 0.048
#> SRR1316096 2 0.2266 0.8281 0.000 0.912 0.084 0.004
#> SRR1408512 1 0.4631 0.6411 0.728 0.004 0.260 0.008
#> SRR1447547 3 0.5926 0.6240 0.088 0.008 0.704 0.200
#> SRR1354053 4 0.4897 0.6194 0.000 0.332 0.008 0.660
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.1121 0.6580 0.956 0.000 0.000 0.000 0.044
#> SRR1349562 1 0.2424 0.5984 0.868 0.000 0.000 0.000 0.132
#> SRR1353376 3 0.4534 0.6425 0.000 0.016 0.764 0.056 0.164
#> SRR1499040 1 0.6948 0.2891 0.484 0.004 0.268 0.012 0.232
#> SRR1322312 1 0.2648 0.5701 0.848 0.000 0.000 0.000 0.152
#> SRR1324412 3 0.4418 0.5127 0.332 0.000 0.652 0.000 0.016
#> SRR1100991 3 0.4592 0.5059 0.332 0.000 0.644 0.000 0.024
#> SRR1349479 3 0.4361 0.6460 0.000 0.016 0.776 0.048 0.160
#> SRR1431248 1 0.6772 0.4724 0.528 0.008 0.276 0.012 0.176
#> SRR1405054 3 0.5728 0.2145 0.432 0.000 0.484 0.000 0.084
#> SRR1312266 1 0.0404 0.6708 0.988 0.000 0.000 0.000 0.012
#> SRR1409790 3 0.4418 0.5127 0.332 0.000 0.652 0.000 0.016
#> SRR1352507 3 0.4418 0.5127 0.332 0.000 0.652 0.000 0.016
#> SRR1383763 5 0.5624 1.0000 0.320 0.004 0.084 0.000 0.592
#> SRR1468314 2 0.4687 0.6653 0.000 0.772 0.136 0.044 0.048
#> SRR1473674 2 0.2878 0.8342 0.000 0.888 0.048 0.016 0.048
#> SRR1390499 1 0.0880 0.6629 0.968 0.000 0.000 0.000 0.032
#> SRR821043 4 0.3667 0.7717 0.000 0.140 0.000 0.812 0.048
#> SRR1455653 4 0.5185 0.5171 0.000 0.384 0.000 0.568 0.048
#> SRR1335236 2 0.0833 0.8399 0.000 0.976 0.016 0.004 0.004
#> SRR1095383 2 0.5719 0.2993 0.000 0.604 0.004 0.288 0.104
#> SRR1479489 1 0.2359 0.6768 0.904 0.000 0.060 0.000 0.036
#> SRR1310433 2 0.0671 0.8391 0.000 0.980 0.016 0.004 0.000
#> SRR1073435 3 0.4661 0.6595 0.028 0.084 0.776 0.000 0.112
#> SRR659649 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.1493 0.6860 0.948 0.000 0.024 0.000 0.028
#> SRR1105248 3 0.5498 0.6632 0.060 0.004 0.732 0.100 0.104
#> SRR1338257 1 0.2325 0.6803 0.904 0.000 0.028 0.000 0.068
#> SRR1499395 3 0.0880 0.7254 0.032 0.000 0.968 0.000 0.000
#> SRR1350002 2 0.3681 0.8138 0.000 0.848 0.052 0.044 0.056
#> SRR1489757 3 0.4418 0.5127 0.332 0.000 0.652 0.000 0.016
#> SRR1414637 1 0.7252 0.4915 0.536 0.008 0.208 0.048 0.200
#> SRR1478113 4 0.2533 0.7758 0.008 0.008 0.000 0.888 0.096
#> SRR1322477 1 0.5340 0.6002 0.700 0.004 0.180 0.008 0.108
#> SRR1478789 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1414185 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 2 0.0833 0.8399 0.000 0.976 0.016 0.004 0.004
#> SRR1376852 1 0.3051 0.6820 0.864 0.000 0.076 0.000 0.060
#> SRR1323491 1 0.0609 0.6721 0.980 0.000 0.000 0.000 0.020
#> SRR1338103 1 0.5933 0.4694 0.556 0.004 0.332 0.000 0.108
#> SRR1472012 1 0.6607 0.4728 0.540 0.004 0.232 0.008 0.216
#> SRR1340325 1 0.2632 0.6780 0.888 0.000 0.040 0.000 0.072
#> SRR1087321 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0794 0.6649 0.972 0.000 0.000 0.000 0.028
#> SRR1334866 1 0.6949 0.5047 0.552 0.004 0.204 0.036 0.204
#> SRR1089446 3 0.5812 0.1864 0.432 0.000 0.476 0.000 0.092
#> SRR1344445 3 0.4065 0.5853 0.264 0.000 0.720 0.000 0.016
#> SRR1412969 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1071668 3 0.5678 0.3318 0.392 0.000 0.524 0.000 0.084
#> SRR1075804 1 0.3116 0.6822 0.860 0.000 0.064 0.000 0.076
#> SRR1383283 3 0.4661 0.6595 0.028 0.084 0.776 0.000 0.112
#> SRR1350239 3 0.6087 0.6317 0.072 0.004 0.684 0.112 0.128
#> SRR1353878 1 0.1300 0.6832 0.956 0.000 0.016 0.000 0.028
#> SRR1375721 1 0.2561 0.5798 0.856 0.000 0.000 0.000 0.144
#> SRR1083983 1 0.6527 0.5049 0.564 0.004 0.236 0.012 0.184
#> SRR1090095 1 0.2561 0.5774 0.856 0.000 0.000 0.000 0.144
#> SRR1414792 1 0.2471 0.5842 0.864 0.000 0.000 0.000 0.136
#> SRR1075102 4 0.2533 0.7758 0.008 0.008 0.000 0.888 0.096
#> SRR1098737 1 0.3116 0.6822 0.860 0.000 0.064 0.000 0.076
#> SRR1349409 1 0.2648 0.5701 0.848 0.000 0.000 0.000 0.152
#> SRR1413008 3 0.6087 0.6317 0.072 0.004 0.684 0.112 0.128
#> SRR1407179 1 0.6304 0.1873 0.432 0.004 0.432 0.000 0.132
#> SRR1095913 3 0.4671 0.6558 0.028 0.104 0.776 0.000 0.092
#> SRR1403544 1 0.2127 0.6231 0.892 0.000 0.000 0.000 0.108
#> SRR1490546 1 0.0404 0.6711 0.988 0.000 0.000 0.000 0.012
#> SRR807971 3 0.4065 0.5853 0.264 0.000 0.720 0.000 0.016
#> SRR1436228 1 0.6326 0.2434 0.452 0.004 0.408 0.000 0.136
#> SRR1445218 2 0.0671 0.8391 0.000 0.980 0.016 0.004 0.000
#> SRR1485438 2 0.3681 0.8138 0.000 0.848 0.052 0.044 0.056
#> SRR1358143 1 0.2605 0.5733 0.852 0.000 0.000 0.000 0.148
#> SRR1328760 1 0.1300 0.6832 0.956 0.000 0.016 0.000 0.028
#> SRR1380806 1 0.2513 0.6163 0.876 0.000 0.008 0.000 0.116
#> SRR1379426 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 1 0.7275 0.4863 0.532 0.008 0.212 0.048 0.200
#> SRR1346734 4 0.1197 0.8077 0.000 0.048 0.000 0.952 0.000
#> SRR1414515 1 0.2329 0.6011 0.876 0.000 0.000 0.000 0.124
#> SRR1082151 1 0.5900 0.5749 0.652 0.004 0.176 0.012 0.156
#> SRR1349320 4 0.2533 0.7758 0.008 0.008 0.000 0.888 0.096
#> SRR1317554 4 0.4615 0.7013 0.000 0.252 0.000 0.700 0.048
#> SRR1076022 2 0.1372 0.8344 0.000 0.956 0.016 0.004 0.024
#> SRR1339573 3 0.2020 0.7022 0.100 0.000 0.900 0.000 0.000
#> SRR1455878 1 0.3354 0.6770 0.844 0.000 0.088 0.000 0.068
#> SRR1446203 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1387397 1 0.5689 0.5336 0.616 0.000 0.248 0.000 0.136
#> SRR1402590 1 0.1121 0.6580 0.956 0.000 0.000 0.000 0.044
#> SRR1317532 1 0.2719 0.6866 0.884 0.000 0.068 0.000 0.048
#> SRR1331488 1 0.1569 0.6782 0.948 0.000 0.012 0.008 0.032
#> SRR1499675 1 0.5844 0.4614 0.556 0.004 0.344 0.000 0.096
#> SRR1440467 3 0.4218 0.6520 0.000 0.016 0.784 0.040 0.160
#> SRR807995 2 0.3537 0.8189 0.000 0.856 0.052 0.040 0.052
#> SRR1476485 4 0.1197 0.8077 0.000 0.048 0.000 0.952 0.000
#> SRR1388214 1 0.3464 0.6648 0.836 0.000 0.068 0.000 0.096
#> SRR1456051 1 0.0794 0.6648 0.972 0.000 0.000 0.000 0.028
#> SRR1473275 3 0.3163 0.6652 0.164 0.000 0.824 0.000 0.012
#> SRR1444083 1 0.2300 0.6793 0.904 0.000 0.024 0.000 0.072
#> SRR1313807 3 0.4825 0.6497 0.028 0.104 0.764 0.000 0.104
#> SRR1470751 1 0.5900 0.5749 0.652 0.004 0.176 0.012 0.156
#> SRR1403434 3 0.3587 0.6782 0.000 0.012 0.824 0.024 0.140
#> SRR1390540 1 0.0510 0.6701 0.984 0.000 0.000 0.000 0.016
#> SRR1093861 2 0.2464 0.8391 0.000 0.904 0.044 0.004 0.048
#> SRR1325290 1 0.6686 0.4729 0.540 0.004 0.228 0.012 0.216
#> SRR1070689 1 0.2424 0.5984 0.868 0.000 0.000 0.000 0.132
#> SRR1384049 5 0.5624 1.0000 0.320 0.004 0.084 0.000 0.592
#> SRR1081184 1 0.2424 0.5984 0.868 0.000 0.000 0.000 0.132
#> SRR1324295 1 0.2179 0.6201 0.888 0.000 0.000 0.000 0.112
#> SRR1365313 3 0.6125 -0.0763 0.404 0.004 0.480 0.000 0.112
#> SRR1321877 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR815711 3 0.5697 0.2978 0.404 0.000 0.512 0.000 0.084
#> SRR1433476 3 0.4506 0.6426 0.000 0.016 0.764 0.052 0.168
#> SRR1101883 3 0.4157 0.5829 0.264 0.000 0.716 0.000 0.020
#> SRR1433729 3 0.5995 0.5190 0.012 0.220 0.640 0.008 0.120
#> SRR1341877 1 0.5933 0.4694 0.556 0.004 0.332 0.000 0.108
#> SRR1090556 1 0.6192 0.4277 0.520 0.004 0.344 0.000 0.132
#> SRR1357389 3 0.5203 0.4735 0.332 0.000 0.608 0.000 0.060
#> SRR1404227 3 0.4671 0.6558 0.028 0.104 0.776 0.000 0.092
#> SRR1376830 1 0.0451 0.6745 0.988 0.000 0.004 0.000 0.008
#> SRR1500661 1 0.2605 0.5868 0.852 0.000 0.000 0.000 0.148
#> SRR1080294 2 0.5719 0.2993 0.000 0.604 0.004 0.288 0.104
#> SRR1336314 4 0.1197 0.8077 0.000 0.048 0.000 0.952 0.000
#> SRR1102152 1 0.3535 0.6572 0.832 0.000 0.088 0.000 0.080
#> SRR1345244 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 1 0.7227 0.2504 0.420 0.008 0.320 0.012 0.240
#> SRR1443776 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1080117 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.2990 0.7807 0.000 0.876 0.012 0.032 0.080
#> SRR1091865 1 0.2654 0.6803 0.888 0.000 0.048 0.000 0.064
#> SRR1361072 1 0.0404 0.6711 0.988 0.000 0.000 0.000 0.012
#> SRR1487890 1 0.2329 0.6054 0.876 0.000 0.000 0.000 0.124
#> SRR1349456 3 0.4671 0.6558 0.028 0.104 0.776 0.000 0.092
#> SRR1389384 1 0.5900 0.5749 0.652 0.004 0.176 0.012 0.156
#> SRR1316096 2 0.2230 0.8405 0.000 0.912 0.044 0.000 0.044
#> SRR1408512 1 0.4909 0.6070 0.716 0.000 0.164 0.000 0.120
#> SRR1447547 3 0.6499 0.6079 0.068 0.012 0.656 0.120 0.144
#> SRR1354053 4 0.5195 0.5085 0.000 0.388 0.000 0.564 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0937 0.6484 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1349562 1 0.2562 0.6129 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1353376 3 0.6094 0.3641 0.000 0.008 0.496 0.012 0.332 0.152
#> SRR1499040 1 0.7115 -0.5351 0.400 0.000 0.160 0.000 0.324 0.116
#> SRR1322312 1 0.2793 0.5898 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR1324412 3 0.5284 0.3999 0.280 0.000 0.600 0.000 0.112 0.008
#> SRR1100991 3 0.5426 0.3768 0.280 0.000 0.584 0.000 0.128 0.008
#> SRR1349479 3 0.6004 0.3735 0.000 0.008 0.508 0.008 0.320 0.156
#> SRR1431248 5 0.5633 0.7289 0.420 0.000 0.128 0.000 0.448 0.004
#> SRR1405054 3 0.6217 -0.0528 0.360 0.000 0.396 0.000 0.236 0.008
#> SRR1312266 1 0.0146 0.6437 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1409790 3 0.5284 0.3999 0.280 0.000 0.600 0.000 0.112 0.008
#> SRR1352507 3 0.5284 0.3999 0.280 0.000 0.600 0.000 0.112 0.008
#> SRR1383763 6 0.3312 1.0000 0.180 0.000 0.028 0.000 0.000 0.792
#> SRR1468314 2 0.5421 0.6309 0.000 0.716 0.072 0.040 0.112 0.060
#> SRR1473674 2 0.2290 0.8134 0.000 0.892 0.004 0.000 0.084 0.020
#> SRR1390499 1 0.0790 0.6491 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR821043 4 0.3703 0.7206 0.000 0.084 0.000 0.816 0.072 0.028
#> SRR1455653 4 0.5559 0.4314 0.000 0.376 0.000 0.524 0.072 0.028
#> SRR1335236 2 0.0436 0.8222 0.000 0.988 0.004 0.004 0.004 0.000
#> SRR1095383 2 0.6315 0.2666 0.000 0.532 0.000 0.244 0.176 0.048
#> SRR1479489 1 0.2492 0.5679 0.888 0.000 0.036 0.000 0.068 0.008
#> SRR1310433 2 0.0291 0.8214 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1073435 3 0.5284 0.5172 0.012 0.016 0.656 0.000 0.228 0.088
#> SRR659649 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1395999 1 0.1327 0.6253 0.936 0.000 0.000 0.000 0.064 0.000
#> SRR1105248 3 0.5361 0.5357 0.004 0.000 0.600 0.092 0.292 0.012
#> SRR1338257 1 0.2118 0.5606 0.888 0.000 0.000 0.000 0.104 0.008
#> SRR1499395 3 0.0909 0.6547 0.020 0.000 0.968 0.000 0.012 0.000
#> SRR1350002 2 0.2932 0.7885 0.000 0.840 0.004 0.000 0.132 0.024
#> SRR1489757 3 0.5284 0.3999 0.280 0.000 0.600 0.000 0.112 0.008
#> SRR1414637 5 0.4808 0.7280 0.408 0.000 0.056 0.000 0.536 0.000
#> SRR1478113 4 0.2454 0.7364 0.000 0.000 0.000 0.840 0.160 0.000
#> SRR1322477 1 0.4763 -0.3710 0.592 0.000 0.064 0.000 0.344 0.000
#> SRR1478789 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1414185 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1069141 2 0.0436 0.8222 0.000 0.988 0.004 0.004 0.004 0.000
#> SRR1376852 1 0.2980 0.4530 0.808 0.000 0.012 0.000 0.180 0.000
#> SRR1323491 1 0.0405 0.6452 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1338103 1 0.6001 -0.6265 0.448 0.004 0.208 0.000 0.340 0.000
#> SRR1472012 5 0.5931 0.7365 0.424 0.000 0.092 0.000 0.448 0.036
#> SRR1340325 1 0.2520 0.5413 0.872 0.000 0.012 0.000 0.108 0.008
#> SRR1087321 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1488790 1 0.0790 0.6495 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR1334866 5 0.4829 0.7302 0.424 0.000 0.056 0.000 0.520 0.000
#> SRR1089446 3 0.6313 -0.1196 0.368 0.000 0.380 0.000 0.240 0.012
#> SRR1344445 3 0.4762 0.4827 0.216 0.000 0.676 0.000 0.104 0.004
#> SRR1412969 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1071668 3 0.6165 0.0823 0.324 0.000 0.436 0.000 0.232 0.008
#> SRR1075804 1 0.2902 0.4152 0.800 0.000 0.004 0.000 0.196 0.000
#> SRR1383283 3 0.5284 0.5172 0.012 0.016 0.656 0.000 0.228 0.088
#> SRR1350239 3 0.5816 0.5068 0.016 0.000 0.560 0.104 0.308 0.012
#> SRR1353878 1 0.1462 0.6085 0.936 0.000 0.000 0.000 0.056 0.008
#> SRR1375721 1 0.2902 0.5941 0.800 0.000 0.000 0.000 0.004 0.196
#> SRR1083983 5 0.5187 0.7317 0.440 0.000 0.088 0.000 0.472 0.000
#> SRR1090095 1 0.2664 0.6026 0.816 0.000 0.000 0.000 0.000 0.184
#> SRR1414792 1 0.2597 0.6085 0.824 0.000 0.000 0.000 0.000 0.176
#> SRR1075102 4 0.2454 0.7364 0.000 0.000 0.000 0.840 0.160 0.000
#> SRR1098737 1 0.2902 0.4152 0.800 0.000 0.004 0.000 0.196 0.000
#> SRR1349409 1 0.2793 0.5898 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR1413008 3 0.5816 0.5068 0.016 0.000 0.560 0.104 0.308 0.012
#> SRR1407179 5 0.6360 0.5997 0.328 0.004 0.304 0.000 0.360 0.004
#> SRR1095913 3 0.5417 0.5256 0.012 0.036 0.672 0.000 0.192 0.088
#> SRR1403544 1 0.2340 0.6232 0.852 0.000 0.000 0.000 0.000 0.148
#> SRR1490546 1 0.0260 0.6459 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR807971 3 0.4762 0.4827 0.216 0.000 0.676 0.000 0.104 0.004
#> SRR1436228 5 0.6436 0.6194 0.348 0.004 0.284 0.000 0.356 0.008
#> SRR1445218 2 0.0291 0.8214 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1485438 2 0.2932 0.7885 0.000 0.840 0.004 0.000 0.132 0.024
#> SRR1358143 1 0.2762 0.5926 0.804 0.000 0.000 0.000 0.000 0.196
#> SRR1328760 1 0.1462 0.6085 0.936 0.000 0.000 0.000 0.056 0.008
#> SRR1380806 1 0.2743 0.6155 0.828 0.000 0.008 0.000 0.000 0.164
#> SRR1379426 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1087007 3 0.0363 0.6563 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1086256 5 0.4851 0.7309 0.404 0.000 0.060 0.000 0.536 0.000
#> SRR1346734 4 0.0000 0.7552 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.2668 0.6149 0.828 0.000 0.000 0.000 0.004 0.168
#> SRR1082151 1 0.4660 -0.5073 0.540 0.000 0.044 0.000 0.416 0.000
#> SRR1349320 4 0.2454 0.7364 0.000 0.000 0.000 0.840 0.160 0.000
#> SRR1317554 4 0.5058 0.6331 0.000 0.240 0.000 0.660 0.072 0.028
#> SRR1076022 2 0.2238 0.7850 0.000 0.900 0.004 0.004 0.076 0.016
#> SRR1339573 3 0.2512 0.6363 0.060 0.000 0.880 0.000 0.060 0.000
#> SRR1455878 1 0.3315 0.3919 0.780 0.000 0.020 0.000 0.200 0.000
#> SRR1446203 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1387397 1 0.5552 -0.4947 0.532 0.000 0.136 0.000 0.328 0.004
#> SRR1402590 1 0.0937 0.6484 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1317532 1 0.2768 0.4891 0.832 0.000 0.012 0.000 0.156 0.000
#> SRR1331488 1 0.1230 0.6354 0.956 0.000 0.000 0.008 0.028 0.008
#> SRR1499675 1 0.6061 -0.6047 0.448 0.004 0.236 0.000 0.312 0.000
#> SRR1440467 3 0.5774 0.3785 0.000 0.008 0.516 0.000 0.320 0.156
#> SRR807995 2 0.2848 0.7936 0.000 0.848 0.004 0.000 0.124 0.024
#> SRR1476485 4 0.0000 0.7552 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.2814 0.4523 0.820 0.000 0.000 0.000 0.172 0.008
#> SRR1456051 1 0.0713 0.6494 0.972 0.000 0.000 0.000 0.000 0.028
#> SRR1473275 3 0.3595 0.5909 0.120 0.000 0.796 0.000 0.084 0.000
#> SRR1444083 1 0.2118 0.5603 0.888 0.000 0.000 0.000 0.104 0.008
#> SRR1313807 3 0.5599 0.5076 0.012 0.036 0.644 0.000 0.220 0.088
#> SRR1470751 1 0.4660 -0.5073 0.540 0.000 0.044 0.000 0.416 0.000
#> SRR1403434 3 0.4737 0.5076 0.000 0.008 0.664 0.000 0.256 0.072
#> SRR1390540 1 0.0363 0.6468 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1093861 2 0.2069 0.8179 0.000 0.908 0.004 0.000 0.068 0.020
#> SRR1325290 5 0.5895 0.7345 0.424 0.000 0.088 0.000 0.452 0.036
#> SRR1070689 1 0.2562 0.6129 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1384049 6 0.3312 1.0000 0.180 0.000 0.028 0.000 0.000 0.792
#> SRR1081184 1 0.2562 0.6129 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1324295 1 0.2378 0.6221 0.848 0.000 0.000 0.000 0.000 0.152
#> SRR1365313 3 0.6430 -0.5440 0.312 0.004 0.380 0.000 0.296 0.008
#> SRR1321877 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR815711 3 0.6183 0.0395 0.336 0.000 0.424 0.000 0.232 0.008
#> SRR1433476 3 0.6033 0.3643 0.000 0.008 0.496 0.008 0.332 0.156
#> SRR1101883 3 0.4803 0.4793 0.216 0.000 0.672 0.000 0.108 0.004
#> SRR1433729 3 0.6695 0.3552 0.000 0.156 0.484 0.000 0.280 0.080
#> SRR1341877 1 0.6001 -0.6265 0.448 0.004 0.208 0.000 0.340 0.000
#> SRR1090556 1 0.6197 -0.6576 0.412 0.004 0.220 0.000 0.360 0.004
#> SRR1357389 3 0.5737 0.3215 0.276 0.000 0.544 0.000 0.172 0.008
#> SRR1404227 3 0.5417 0.5256 0.012 0.036 0.672 0.000 0.192 0.088
#> SRR1376830 1 0.0820 0.6464 0.972 0.000 0.000 0.000 0.012 0.016
#> SRR1500661 1 0.3014 0.5971 0.804 0.000 0.000 0.000 0.012 0.184
#> SRR1080294 2 0.6315 0.2666 0.000 0.532 0.000 0.244 0.176 0.048
#> SRR1336314 4 0.0000 0.7552 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1102152 1 0.3316 0.4431 0.812 0.000 0.028 0.000 0.152 0.008
#> SRR1345244 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1478637 5 0.7197 0.6055 0.320 0.000 0.192 0.000 0.380 0.108
#> SRR1443776 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1120939 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1080117 3 0.0000 0.6579 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1102899 2 0.3472 0.7274 0.000 0.812 0.004 0.004 0.136 0.044
#> SRR1091865 1 0.2656 0.5295 0.860 0.000 0.012 0.000 0.120 0.008
#> SRR1361072 1 0.0146 0.6442 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1487890 1 0.2562 0.6124 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1349456 3 0.5417 0.5256 0.012 0.036 0.672 0.000 0.192 0.088
#> SRR1389384 1 0.4660 -0.5073 0.540 0.000 0.044 0.000 0.416 0.000
#> SRR1316096 2 0.1951 0.8194 0.000 0.916 0.004 0.000 0.060 0.020
#> SRR1408512 1 0.4756 -0.1698 0.636 0.000 0.068 0.000 0.292 0.004
#> SRR1447547 3 0.5827 0.4775 0.012 0.000 0.532 0.104 0.340 0.012
#> SRR1354053 4 0.5566 0.4221 0.000 0.380 0.000 0.520 0.072 0.028
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.939 0.934 0.974 0.4932 0.507 0.507
#> 3 3 0.918 0.937 0.968 0.3090 0.732 0.527
#> 4 4 0.658 0.762 0.841 0.1206 0.858 0.627
#> 5 5 0.742 0.697 0.802 0.0738 0.936 0.770
#> 6 6 0.734 0.679 0.766 0.0436 0.929 0.714
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.971 1.000 0.000
#> SRR1349562 1 0.0000 0.971 1.000 0.000
#> SRR1353376 2 0.0000 0.974 0.000 1.000
#> SRR1499040 1 0.0000 0.971 1.000 0.000
#> SRR1322312 1 0.0000 0.971 1.000 0.000
#> SRR1324412 1 0.0000 0.971 1.000 0.000
#> SRR1100991 1 0.0000 0.971 1.000 0.000
#> SRR1349479 2 0.0000 0.974 0.000 1.000
#> SRR1431248 1 0.7883 0.694 0.764 0.236
#> SRR1405054 1 0.0000 0.971 1.000 0.000
#> SRR1312266 1 0.0000 0.971 1.000 0.000
#> SRR1409790 1 0.0000 0.971 1.000 0.000
#> SRR1352507 1 0.0000 0.971 1.000 0.000
#> SRR1383763 1 0.0000 0.971 1.000 0.000
#> SRR1468314 2 0.0000 0.974 0.000 1.000
#> SRR1473674 2 0.0000 0.974 0.000 1.000
#> SRR1390499 1 0.0000 0.971 1.000 0.000
#> SRR821043 2 0.0000 0.974 0.000 1.000
#> SRR1455653 2 0.0000 0.974 0.000 1.000
#> SRR1335236 2 0.0000 0.974 0.000 1.000
#> SRR1095383 2 0.0000 0.974 0.000 1.000
#> SRR1479489 1 0.0000 0.971 1.000 0.000
#> SRR1310433 2 0.0000 0.974 0.000 1.000
#> SRR1073435 2 0.0000 0.974 0.000 1.000
#> SRR659649 2 0.0376 0.972 0.004 0.996
#> SRR1395999 1 0.0000 0.971 1.000 0.000
#> SRR1105248 2 0.0000 0.974 0.000 1.000
#> SRR1338257 1 0.0000 0.971 1.000 0.000
#> SRR1499395 1 0.9427 0.447 0.640 0.360
#> SRR1350002 2 0.0000 0.974 0.000 1.000
#> SRR1489757 1 0.0000 0.971 1.000 0.000
#> SRR1414637 1 0.7219 0.750 0.800 0.200
#> SRR1478113 2 0.0000 0.974 0.000 1.000
#> SRR1322477 1 0.0000 0.971 1.000 0.000
#> SRR1478789 2 0.0376 0.972 0.004 0.996
#> SRR1414185 2 0.0376 0.972 0.004 0.996
#> SRR1069141 2 0.0000 0.974 0.000 1.000
#> SRR1376852 1 0.0000 0.971 1.000 0.000
#> SRR1323491 1 0.0000 0.971 1.000 0.000
#> SRR1338103 1 0.2043 0.943 0.968 0.032
#> SRR1472012 1 0.0000 0.971 1.000 0.000
#> SRR1340325 1 0.0000 0.971 1.000 0.000
#> SRR1087321 2 0.0000 0.974 0.000 1.000
#> SRR1488790 1 0.0000 0.971 1.000 0.000
#> SRR1334866 2 0.8386 0.626 0.268 0.732
#> SRR1089446 1 0.0000 0.971 1.000 0.000
#> SRR1344445 1 0.0672 0.964 0.992 0.008
#> SRR1412969 2 0.0000 0.974 0.000 1.000
#> SRR1071668 1 0.0000 0.971 1.000 0.000
#> SRR1075804 1 0.0000 0.971 1.000 0.000
#> SRR1383283 2 0.0000 0.974 0.000 1.000
#> SRR1350239 1 0.9922 0.201 0.552 0.448
#> SRR1353878 1 0.0000 0.971 1.000 0.000
#> SRR1375721 1 0.0000 0.971 1.000 0.000
#> SRR1083983 1 0.0000 0.971 1.000 0.000
#> SRR1090095 1 0.0000 0.971 1.000 0.000
#> SRR1414792 1 0.0000 0.971 1.000 0.000
#> SRR1075102 2 0.0000 0.974 0.000 1.000
#> SRR1098737 1 0.0000 0.971 1.000 0.000
#> SRR1349409 1 0.0000 0.971 1.000 0.000
#> SRR1413008 1 0.9922 0.201 0.552 0.448
#> SRR1407179 1 0.6973 0.766 0.812 0.188
#> SRR1095913 2 0.0000 0.974 0.000 1.000
#> SRR1403544 1 0.0000 0.971 1.000 0.000
#> SRR1490546 1 0.0000 0.971 1.000 0.000
#> SRR807971 1 0.0000 0.971 1.000 0.000
#> SRR1436228 2 0.9358 0.449 0.352 0.648
#> SRR1445218 2 0.0000 0.974 0.000 1.000
#> SRR1485438 2 0.0376 0.972 0.004 0.996
#> SRR1358143 1 0.0000 0.971 1.000 0.000
#> SRR1328760 1 0.0000 0.971 1.000 0.000
#> SRR1380806 1 0.0000 0.971 1.000 0.000
#> SRR1379426 2 0.0376 0.972 0.004 0.996
#> SRR1087007 2 0.0376 0.972 0.004 0.996
#> SRR1086256 2 0.0000 0.974 0.000 1.000
#> SRR1346734 2 0.0000 0.974 0.000 1.000
#> SRR1414515 1 0.0000 0.971 1.000 0.000
#> SRR1082151 1 0.0000 0.971 1.000 0.000
#> SRR1349320 2 0.0000 0.974 0.000 1.000
#> SRR1317554 2 0.0000 0.974 0.000 1.000
#> SRR1076022 2 0.0000 0.974 0.000 1.000
#> SRR1339573 2 0.9954 0.127 0.460 0.540
#> SRR1455878 1 0.0000 0.971 1.000 0.000
#> SRR1446203 2 0.0376 0.972 0.004 0.996
#> SRR1387397 1 0.0000 0.971 1.000 0.000
#> SRR1402590 1 0.0000 0.971 1.000 0.000
#> SRR1317532 1 0.0000 0.971 1.000 0.000
#> SRR1331488 1 0.0000 0.971 1.000 0.000
#> SRR1499675 1 0.6887 0.772 0.816 0.184
#> SRR1440467 2 0.0000 0.974 0.000 1.000
#> SRR807995 2 0.0000 0.974 0.000 1.000
#> SRR1476485 2 0.0000 0.974 0.000 1.000
#> SRR1388214 1 0.0000 0.971 1.000 0.000
#> SRR1456051 1 0.0000 0.971 1.000 0.000
#> SRR1473275 1 0.0000 0.971 1.000 0.000
#> SRR1444083 1 0.0000 0.971 1.000 0.000
#> SRR1313807 2 0.0000 0.974 0.000 1.000
#> SRR1470751 1 0.0000 0.971 1.000 0.000
#> SRR1403434 2 0.0000 0.974 0.000 1.000
#> SRR1390540 1 0.0000 0.971 1.000 0.000
#> SRR1093861 2 0.0000 0.974 0.000 1.000
#> SRR1325290 1 0.0000 0.971 1.000 0.000
#> SRR1070689 1 0.0000 0.971 1.000 0.000
#> SRR1384049 1 0.0000 0.971 1.000 0.000
#> SRR1081184 1 0.0000 0.971 1.000 0.000
#> SRR1324295 1 0.0000 0.971 1.000 0.000
#> SRR1365313 2 0.0376 0.972 0.004 0.996
#> SRR1321877 2 0.0376 0.972 0.004 0.996
#> SRR815711 1 0.0000 0.971 1.000 0.000
#> SRR1433476 2 0.0000 0.974 0.000 1.000
#> SRR1101883 1 0.3431 0.912 0.936 0.064
#> SRR1433729 2 0.0000 0.974 0.000 1.000
#> SRR1341877 1 0.0672 0.964 0.992 0.008
#> SRR1090556 1 0.0000 0.971 1.000 0.000
#> SRR1357389 1 0.0000 0.971 1.000 0.000
#> SRR1404227 2 0.0376 0.972 0.004 0.996
#> SRR1376830 1 0.0000 0.971 1.000 0.000
#> SRR1500661 1 0.0000 0.971 1.000 0.000
#> SRR1080294 2 0.0000 0.974 0.000 1.000
#> SRR1336314 2 0.0000 0.974 0.000 1.000
#> SRR1102152 1 0.0000 0.971 1.000 0.000
#> SRR1345244 2 0.0376 0.972 0.004 0.996
#> SRR1478637 2 0.8555 0.604 0.280 0.720
#> SRR1443776 2 0.0376 0.972 0.004 0.996
#> SRR1120939 2 0.0376 0.972 0.004 0.996
#> SRR1080117 2 0.0376 0.972 0.004 0.996
#> SRR1102899 2 0.0000 0.974 0.000 1.000
#> SRR1091865 1 0.0000 0.971 1.000 0.000
#> SRR1361072 1 0.0000 0.971 1.000 0.000
#> SRR1487890 1 0.0000 0.971 1.000 0.000
#> SRR1349456 2 0.0000 0.974 0.000 1.000
#> SRR1389384 1 0.0000 0.971 1.000 0.000
#> SRR1316096 2 0.0000 0.974 0.000 1.000
#> SRR1408512 1 0.0000 0.971 1.000 0.000
#> SRR1447547 2 0.0376 0.972 0.004 0.996
#> SRR1354053 2 0.0000 0.974 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1499040 1 0.4555 0.769 0.800 0.000 0.200
#> SRR1322312 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1324412 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1100991 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1349479 3 0.3038 0.885 0.000 0.104 0.896
#> SRR1431248 1 0.5692 0.651 0.724 0.008 0.268
#> SRR1405054 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1409790 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1352507 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1383763 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1468314 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1473674 2 0.0892 0.985 0.000 0.980 0.020
#> SRR1390499 1 0.0000 0.967 1.000 0.000 0.000
#> SRR821043 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1455653 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1335236 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1095383 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1479489 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1310433 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1073435 3 0.3752 0.838 0.000 0.144 0.856
#> SRR659649 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1105248 3 0.0892 0.945 0.000 0.020 0.980
#> SRR1338257 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1350002 2 0.0892 0.985 0.000 0.980 0.020
#> SRR1489757 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1414637 1 0.4742 0.846 0.848 0.048 0.104
#> SRR1478113 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1322477 1 0.0424 0.962 0.992 0.000 0.008
#> SRR1478789 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1069141 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1376852 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1338103 1 0.4931 0.747 0.784 0.004 0.212
#> SRR1472012 1 0.3551 0.852 0.868 0.000 0.132
#> SRR1340325 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1334866 3 0.0237 0.949 0.000 0.004 0.996
#> SRR1089446 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1344445 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1412969 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1071668 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1075804 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1383283 3 0.4702 0.757 0.000 0.212 0.788
#> SRR1350239 3 0.1031 0.944 0.000 0.024 0.976
#> SRR1353878 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1083983 1 0.1031 0.951 0.976 0.000 0.024
#> SRR1090095 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1413008 3 0.1031 0.944 0.000 0.024 0.976
#> SRR1407179 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1095913 3 0.4504 0.754 0.000 0.196 0.804
#> SRR1403544 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.967 1.000 0.000 0.000
#> SRR807971 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1436228 3 0.4784 0.741 0.200 0.004 0.796
#> SRR1445218 2 0.0592 0.987 0.000 0.988 0.012
#> SRR1485438 2 0.1643 0.965 0.000 0.956 0.044
#> SRR1358143 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1086256 2 0.2165 0.944 0.000 0.936 0.064
#> SRR1346734 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1082151 1 0.2096 0.927 0.944 0.004 0.052
#> SRR1349320 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1317554 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1076022 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1339573 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1387397 1 0.1031 0.951 0.976 0.000 0.024
#> SRR1402590 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1499675 3 0.4796 0.722 0.220 0.000 0.780
#> SRR1440467 3 0.0000 0.950 0.000 0.000 1.000
#> SRR807995 2 0.0892 0.985 0.000 0.980 0.020
#> SRR1476485 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1473275 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1444083 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1313807 3 0.5591 0.592 0.000 0.304 0.696
#> SRR1470751 1 0.0661 0.960 0.988 0.004 0.008
#> SRR1403434 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1093861 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1325290 1 0.2400 0.916 0.932 0.004 0.064
#> SRR1070689 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1365313 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1321877 3 0.0000 0.950 0.000 0.000 1.000
#> SRR815711 3 0.1031 0.944 0.024 0.000 0.976
#> SRR1433476 3 0.3941 0.833 0.000 0.156 0.844
#> SRR1101883 3 0.0892 0.945 0.020 0.000 0.980
#> SRR1433729 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1341877 1 0.6095 0.383 0.608 0.000 0.392
#> SRR1090556 1 0.4887 0.726 0.772 0.000 0.228
#> SRR1357389 3 0.0237 0.950 0.004 0.000 0.996
#> SRR1404227 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1080294 2 0.0237 0.988 0.000 0.996 0.004
#> SRR1336314 2 0.0000 0.987 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1478637 3 0.1753 0.923 0.000 0.048 0.952
#> SRR1443776 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.950 0.000 0.000 1.000
#> SRR1102899 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1091865 1 0.0424 0.962 0.992 0.000 0.008
#> SRR1361072 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.967 1.000 0.000 0.000
#> SRR1349456 3 0.4452 0.760 0.000 0.192 0.808
#> SRR1389384 1 0.2096 0.927 0.944 0.004 0.052
#> SRR1316096 2 0.1031 0.985 0.000 0.976 0.024
#> SRR1408512 1 0.0424 0.962 0.992 0.000 0.008
#> SRR1447547 3 0.1031 0.944 0.000 0.024 0.976
#> SRR1354053 2 0.0237 0.988 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.4567 0.61070 0.000 0.244 0.016 0.740
#> SRR1499040 2 0.7289 0.54675 0.200 0.532 0.268 0.000
#> SRR1322312 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1100991 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1349479 3 0.4868 0.66208 0.000 0.040 0.748 0.212
#> SRR1431248 2 0.5672 0.74493 0.276 0.668 0.056 0.000
#> SRR1405054 1 0.3764 0.77472 0.816 0.172 0.012 0.000
#> SRR1312266 1 0.0336 0.93156 0.992 0.008 0.000 0.000
#> SRR1409790 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1352507 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1383763 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.0817 0.84550 0.000 0.024 0.000 0.976
#> SRR1473674 4 0.4228 0.82984 0.000 0.232 0.008 0.760
#> SRR1390499 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR821043 4 0.1389 0.83495 0.000 0.048 0.000 0.952
#> SRR1455653 4 0.1474 0.83371 0.000 0.052 0.000 0.948
#> SRR1335236 4 0.4468 0.82520 0.000 0.232 0.016 0.752
#> SRR1095383 4 0.0000 0.84462 0.000 0.000 0.000 1.000
#> SRR1479489 1 0.2281 0.87059 0.904 0.096 0.000 0.000
#> SRR1310433 4 0.3726 0.83704 0.000 0.212 0.000 0.788
#> SRR1073435 3 0.7499 -0.05781 0.000 0.400 0.420 0.180
#> SRR659649 3 0.0000 0.82879 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.4500 0.45712 0.684 0.316 0.000 0.000
#> SRR1105248 3 0.5807 0.67844 0.000 0.160 0.708 0.132
#> SRR1338257 1 0.2647 0.84980 0.880 0.120 0.000 0.000
#> SRR1499395 3 0.0188 0.82943 0.000 0.004 0.996 0.000
#> SRR1350002 4 0.4228 0.82984 0.000 0.232 0.008 0.760
#> SRR1489757 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1414637 2 0.5393 0.74511 0.268 0.688 0.044 0.000
#> SRR1478113 2 0.5353 0.15793 0.000 0.556 0.012 0.432
#> SRR1322477 2 0.5311 0.70203 0.328 0.648 0.024 0.000
#> SRR1478789 3 0.0657 0.82831 0.000 0.012 0.984 0.004
#> SRR1414185 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1069141 4 0.4194 0.83119 0.000 0.228 0.008 0.764
#> SRR1376852 1 0.4454 0.44162 0.692 0.308 0.000 0.000
#> SRR1323491 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1338103 2 0.5668 0.73540 0.300 0.652 0.048 0.000
#> SRR1472012 2 0.5636 0.72914 0.308 0.648 0.044 0.000
#> SRR1340325 1 0.2530 0.85569 0.888 0.112 0.000 0.000
#> SRR1087321 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR1488790 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.4776 0.35162 0.000 0.624 0.376 0.000
#> SRR1089446 3 0.4164 0.71830 0.000 0.264 0.736 0.000
#> SRR1344445 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1412969 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1071668 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1075804 1 0.0336 0.93156 0.992 0.008 0.000 0.000
#> SRR1383283 3 0.7883 -0.02943 0.000 0.316 0.384 0.300
#> SRR1350239 3 0.5781 0.68685 0.000 0.252 0.676 0.072
#> SRR1353878 1 0.2647 0.84980 0.880 0.120 0.000 0.000
#> SRR1375721 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1083983 2 0.4564 0.68544 0.328 0.672 0.000 0.000
#> SRR1090095 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1075102 2 0.5408 0.21820 0.000 0.576 0.016 0.408
#> SRR1098737 1 0.0592 0.92748 0.984 0.016 0.000 0.000
#> SRR1349409 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1413008 3 0.5781 0.68685 0.000 0.252 0.676 0.072
#> SRR1407179 3 0.4985 0.15168 0.000 0.468 0.532 0.000
#> SRR1095913 3 0.4399 0.63648 0.000 0.020 0.768 0.212
#> SRR1403544 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR807971 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1436228 2 0.6363 0.64415 0.120 0.672 0.200 0.008
#> SRR1445218 4 0.3726 0.83704 0.000 0.212 0.000 0.788
#> SRR1485438 2 0.4735 0.34216 0.000 0.784 0.068 0.148
#> SRR1358143 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.2530 0.85749 0.888 0.112 0.000 0.000
#> SRR1380806 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1087007 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1086256 2 0.6563 0.48890 0.000 0.632 0.160 0.208
#> SRR1346734 4 0.1637 0.83085 0.000 0.060 0.000 0.940
#> SRR1414515 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.5442 0.73840 0.288 0.672 0.040 0.000
#> SRR1349320 4 0.3873 0.65499 0.000 0.228 0.000 0.772
#> SRR1317554 4 0.1302 0.83624 0.000 0.044 0.000 0.956
#> SRR1076022 4 0.3907 0.83225 0.000 0.232 0.000 0.768
#> SRR1339573 3 0.0188 0.82943 0.000 0.004 0.996 0.000
#> SRR1455878 1 0.3172 0.79823 0.840 0.160 0.000 0.000
#> SRR1446203 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR1387397 2 0.4720 0.69168 0.324 0.672 0.004 0.000
#> SRR1402590 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.3074 0.80816 0.848 0.152 0.000 0.000
#> SRR1331488 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1499675 2 0.6566 0.63165 0.140 0.624 0.236 0.000
#> SRR1440467 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR807995 4 0.4630 0.81195 0.000 0.252 0.016 0.732
#> SRR1476485 4 0.1637 0.83085 0.000 0.060 0.000 0.940
#> SRR1388214 1 0.2921 0.82518 0.860 0.140 0.000 0.000
#> SRR1456051 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.3074 0.79754 0.000 0.152 0.848 0.000
#> SRR1444083 1 0.2973 0.81978 0.856 0.144 0.000 0.000
#> SRR1313807 4 0.5503 -0.00433 0.000 0.016 0.468 0.516
#> SRR1470751 2 0.5321 0.73171 0.296 0.672 0.032 0.000
#> SRR1403434 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1390540 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1093861 4 0.4228 0.82984 0.000 0.232 0.008 0.760
#> SRR1325290 2 0.5578 0.72516 0.312 0.648 0.040 0.000
#> SRR1070689 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.5292 0.12739 0.000 0.512 0.480 0.008
#> SRR1321877 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR815711 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1433476 3 0.6118 0.58242 0.000 0.120 0.672 0.208
#> SRR1101883 3 0.3311 0.79285 0.000 0.172 0.828 0.000
#> SRR1433729 4 0.0592 0.84229 0.000 0.016 0.000 0.984
#> SRR1341877 2 0.6015 0.74261 0.268 0.652 0.080 0.000
#> SRR1090556 2 0.5207 0.72887 0.292 0.680 0.028 0.000
#> SRR1357389 3 0.2760 0.80724 0.000 0.128 0.872 0.000
#> SRR1404227 3 0.2868 0.74853 0.000 0.136 0.864 0.000
#> SRR1376830 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.0000 0.84462 0.000 0.000 0.000 1.000
#> SRR1336314 4 0.1637 0.83085 0.000 0.060 0.000 0.940
#> SRR1102152 1 0.2011 0.88406 0.920 0.080 0.000 0.000
#> SRR1345244 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR1478637 2 0.4855 0.38976 0.000 0.644 0.352 0.004
#> SRR1443776 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR1120939 3 0.0469 0.82955 0.000 0.012 0.988 0.000
#> SRR1080117 3 0.0592 0.83001 0.000 0.016 0.984 0.000
#> SRR1102899 4 0.3726 0.83704 0.000 0.212 0.000 0.788
#> SRR1091865 2 0.4564 0.68544 0.328 0.672 0.000 0.000
#> SRR1361072 1 0.1022 0.91764 0.968 0.032 0.000 0.000
#> SRR1487890 1 0.0000 0.93579 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.4472 0.62482 0.000 0.020 0.760 0.220
#> SRR1389384 2 0.5489 0.73503 0.296 0.664 0.040 0.000
#> SRR1316096 4 0.3873 0.83329 0.000 0.228 0.000 0.772
#> SRR1408512 2 0.4741 0.68906 0.328 0.668 0.004 0.000
#> SRR1447547 2 0.5714 0.52629 0.000 0.716 0.156 0.128
#> SRR1354053 4 0.0817 0.84148 0.000 0.024 0.000 0.976
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0703 0.8914 0.976 0.024 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4031 0.4529 0.000 0.044 0.000 0.772 0.184
#> SRR1499040 5 0.5704 0.6120 0.036 0.072 0.228 0.000 0.664
#> SRR1322312 1 0.1282 0.8728 0.952 0.044 0.000 0.000 0.004
#> SRR1324412 3 0.2236 0.7371 0.000 0.024 0.908 0.000 0.068
#> SRR1100991 3 0.2491 0.7285 0.000 0.036 0.896 0.000 0.068
#> SRR1349479 3 0.6961 0.3623 0.000 0.224 0.416 0.348 0.012
#> SRR1431248 5 0.1331 0.8308 0.040 0.008 0.000 0.000 0.952
#> SRR1405054 1 0.6962 0.5942 0.556 0.124 0.248 0.000 0.072
#> SRR1312266 1 0.2707 0.8754 0.876 0.100 0.000 0.000 0.024
#> SRR1409790 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1352507 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1383763 1 0.1357 0.8714 0.948 0.048 0.000 0.000 0.004
#> SRR1468314 4 0.3809 0.2504 0.000 0.256 0.000 0.736 0.008
#> SRR1473674 2 0.4752 0.6988 0.000 0.568 0.000 0.412 0.020
#> SRR1390499 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.1121 0.5106 0.000 0.044 0.000 0.956 0.000
#> SRR1455653 4 0.1121 0.5106 0.000 0.044 0.000 0.956 0.000
#> SRR1335236 2 0.4235 0.7258 0.000 0.576 0.000 0.424 0.000
#> SRR1095383 4 0.3783 0.2619 0.000 0.252 0.000 0.740 0.008
#> SRR1479489 1 0.4814 0.8211 0.764 0.128 0.076 0.000 0.032
#> SRR1310433 2 0.4262 0.7239 0.000 0.560 0.000 0.440 0.000
#> SRR1073435 5 0.6433 0.5127 0.000 0.172 0.144 0.056 0.628
#> SRR659649 3 0.3769 0.7930 0.000 0.180 0.788 0.000 0.032
#> SRR1395999 1 0.5902 0.4592 0.556 0.124 0.000 0.000 0.320
#> SRR1105248 3 0.6946 0.3062 0.000 0.140 0.468 0.356 0.036
#> SRR1338257 1 0.5436 0.7944 0.728 0.124 0.080 0.000 0.068
#> SRR1499395 3 0.4073 0.7951 0.000 0.216 0.752 0.000 0.032
#> SRR1350002 2 0.4893 0.6908 0.000 0.568 0.000 0.404 0.028
#> SRR1489757 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1414637 5 0.1913 0.8286 0.044 0.016 0.000 0.008 0.932
#> SRR1478113 4 0.4630 0.1926 0.000 0.016 0.000 0.588 0.396
#> SRR1322477 5 0.2067 0.8312 0.048 0.032 0.000 0.000 0.920
#> SRR1478789 3 0.4424 0.7848 0.000 0.224 0.728 0.000 0.048
#> SRR1414185 3 0.4073 0.7951 0.000 0.216 0.752 0.000 0.032
#> SRR1069141 2 0.4249 0.7296 0.000 0.568 0.000 0.432 0.000
#> SRR1376852 5 0.5810 0.0339 0.428 0.092 0.000 0.000 0.480
#> SRR1323491 1 0.1851 0.8857 0.912 0.088 0.000 0.000 0.000
#> SRR1338103 5 0.1764 0.8306 0.064 0.008 0.000 0.000 0.928
#> SRR1472012 5 0.1544 0.8299 0.068 0.000 0.000 0.000 0.932
#> SRR1340325 1 0.5158 0.8054 0.744 0.124 0.088 0.000 0.044
#> SRR1087321 3 0.4104 0.7944 0.000 0.220 0.748 0.000 0.032
#> SRR1488790 1 0.0703 0.8916 0.976 0.024 0.000 0.000 0.000
#> SRR1334866 5 0.1493 0.8004 0.000 0.028 0.024 0.000 0.948
#> SRR1089446 3 0.3756 0.5463 0.000 0.008 0.744 0.000 0.248
#> SRR1344445 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1412969 3 0.4104 0.7943 0.000 0.220 0.748 0.000 0.032
#> SRR1071668 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1075804 1 0.2824 0.8740 0.872 0.096 0.000 0.000 0.032
#> SRR1383283 5 0.7749 0.2806 0.000 0.196 0.144 0.168 0.492
#> SRR1350239 3 0.4049 0.6498 0.000 0.000 0.792 0.124 0.084
#> SRR1353878 1 0.5380 0.7977 0.732 0.124 0.076 0.000 0.068
#> SRR1375721 1 0.0703 0.8851 0.976 0.024 0.000 0.000 0.000
#> SRR1083983 5 0.3151 0.8082 0.068 0.064 0.004 0.000 0.864
#> SRR1090095 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.4527 0.2082 0.000 0.012 0.000 0.596 0.392
#> SRR1098737 1 0.2879 0.8727 0.868 0.100 0.000 0.000 0.032
#> SRR1349409 1 0.0703 0.8851 0.976 0.024 0.000 0.000 0.000
#> SRR1413008 3 0.4049 0.6498 0.000 0.000 0.792 0.124 0.084
#> SRR1407179 5 0.5043 0.5821 0.000 0.136 0.160 0.000 0.704
#> SRR1095913 3 0.6508 0.4774 0.000 0.400 0.484 0.064 0.052
#> SRR1403544 1 0.0162 0.8900 0.996 0.004 0.000 0.000 0.000
#> SRR1490546 1 0.2020 0.8814 0.900 0.100 0.000 0.000 0.000
#> SRR807971 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1436228 5 0.1498 0.8250 0.024 0.016 0.008 0.000 0.952
#> SRR1445218 2 0.4262 0.7239 0.000 0.560 0.000 0.440 0.000
#> SRR1485438 5 0.4430 0.4706 0.000 0.360 0.000 0.012 0.628
#> SRR1358143 1 0.1282 0.8728 0.952 0.044 0.000 0.000 0.004
#> SRR1328760 1 0.5263 0.8041 0.740 0.124 0.072 0.000 0.064
#> SRR1380806 1 0.0290 0.8907 0.992 0.008 0.000 0.000 0.000
#> SRR1379426 3 0.4073 0.7951 0.000 0.216 0.752 0.000 0.032
#> SRR1087007 3 0.4163 0.7916 0.000 0.228 0.740 0.000 0.032
#> SRR1086256 5 0.2313 0.7871 0.000 0.032 0.012 0.040 0.916
#> SRR1346734 4 0.0162 0.5256 0.000 0.000 0.000 0.996 0.004
#> SRR1414515 1 0.0703 0.8851 0.976 0.024 0.000 0.000 0.000
#> SRR1082151 5 0.2438 0.8288 0.044 0.040 0.000 0.008 0.908
#> SRR1349320 4 0.3048 0.4690 0.000 0.004 0.000 0.820 0.176
#> SRR1317554 4 0.3231 0.3587 0.000 0.196 0.000 0.800 0.004
#> SRR1076022 2 0.4249 0.7296 0.000 0.568 0.000 0.432 0.000
#> SRR1339573 3 0.4010 0.7954 0.000 0.208 0.760 0.000 0.032
#> SRR1455878 1 0.7098 0.4941 0.536 0.120 0.080 0.000 0.264
#> SRR1446203 3 0.4104 0.7944 0.000 0.220 0.748 0.000 0.032
#> SRR1387397 5 0.5018 0.7340 0.064 0.108 0.068 0.000 0.760
#> SRR1402590 1 0.0162 0.8900 0.996 0.004 0.000 0.000 0.000
#> SRR1317532 1 0.5956 0.7534 0.688 0.124 0.080 0.000 0.108
#> SRR1331488 1 0.2179 0.8836 0.896 0.100 0.000 0.000 0.004
#> SRR1499675 5 0.2777 0.8146 0.040 0.036 0.028 0.000 0.896
#> SRR1440467 3 0.4210 0.7918 0.000 0.224 0.740 0.000 0.036
#> SRR807995 2 0.5019 0.6783 0.000 0.568 0.000 0.396 0.036
#> SRR1476485 4 0.0162 0.5256 0.000 0.000 0.000 0.996 0.004
#> SRR1388214 1 0.5550 0.7870 0.720 0.124 0.080 0.000 0.076
#> SRR1456051 1 0.1792 0.8853 0.916 0.084 0.000 0.000 0.000
#> SRR1473275 3 0.1671 0.7557 0.000 0.000 0.924 0.000 0.076
#> SRR1444083 1 0.5550 0.7864 0.720 0.124 0.080 0.000 0.076
#> SRR1313807 2 0.7070 -0.1645 0.000 0.412 0.152 0.400 0.036
#> SRR1470751 5 0.2438 0.8288 0.044 0.040 0.000 0.008 0.908
#> SRR1403434 3 0.4210 0.7918 0.000 0.224 0.740 0.000 0.036
#> SRR1390540 1 0.1851 0.8857 0.912 0.088 0.000 0.000 0.000
#> SRR1093861 2 0.4249 0.7296 0.000 0.568 0.000 0.432 0.000
#> SRR1325290 5 0.1704 0.8301 0.068 0.004 0.000 0.000 0.928
#> SRR1070689 1 0.0162 0.8900 0.996 0.004 0.000 0.000 0.000
#> SRR1384049 1 0.1357 0.8714 0.948 0.048 0.000 0.000 0.004
#> SRR1081184 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8900 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 5 0.4417 0.6404 0.000 0.148 0.092 0.000 0.760
#> SRR1321877 3 0.4104 0.7944 0.000 0.220 0.748 0.000 0.032
#> SRR815711 3 0.2136 0.7409 0.000 0.008 0.904 0.000 0.088
#> SRR1433476 4 0.7449 -0.2430 0.000 0.196 0.352 0.404 0.048
#> SRR1101883 3 0.1544 0.7513 0.000 0.000 0.932 0.000 0.068
#> SRR1433729 4 0.3890 0.2851 0.000 0.252 0.000 0.736 0.012
#> SRR1341877 5 0.1956 0.8321 0.052 0.012 0.008 0.000 0.928
#> SRR1090556 5 0.2353 0.8271 0.060 0.028 0.004 0.000 0.908
#> SRR1357389 3 0.0794 0.7584 0.000 0.000 0.972 0.000 0.028
#> SRR1404227 3 0.6616 0.2868 0.000 0.216 0.404 0.000 0.380
#> SRR1376830 1 0.1478 0.8886 0.936 0.064 0.000 0.000 0.000
#> SRR1500661 1 0.0510 0.8914 0.984 0.016 0.000 0.000 0.000
#> SRR1080294 4 0.3783 0.2619 0.000 0.252 0.000 0.740 0.008
#> SRR1336314 4 0.0579 0.5229 0.000 0.008 0.000 0.984 0.008
#> SRR1102152 1 0.4649 0.8262 0.772 0.128 0.076 0.000 0.024
#> SRR1345244 3 0.4073 0.7951 0.000 0.216 0.752 0.000 0.032
#> SRR1478637 5 0.1300 0.8081 0.000 0.016 0.028 0.000 0.956
#> SRR1443776 3 0.4104 0.7944 0.000 0.220 0.748 0.000 0.032
#> SRR1120939 3 0.4104 0.7944 0.000 0.220 0.748 0.000 0.032
#> SRR1080117 3 0.4073 0.7951 0.000 0.216 0.752 0.000 0.032
#> SRR1102899 2 0.4415 0.7161 0.000 0.552 0.000 0.444 0.004
#> SRR1091865 5 0.3465 0.7936 0.052 0.104 0.004 0.000 0.840
#> SRR1361072 1 0.3590 0.8582 0.828 0.128 0.036 0.000 0.008
#> SRR1487890 1 0.0290 0.8894 0.992 0.008 0.000 0.000 0.000
#> SRR1349456 2 0.6515 -0.4359 0.000 0.444 0.440 0.072 0.044
#> SRR1389384 5 0.2228 0.8305 0.048 0.040 0.000 0.000 0.912
#> SRR1316096 2 0.4256 0.7278 0.000 0.564 0.000 0.436 0.000
#> SRR1408512 5 0.3567 0.7869 0.068 0.092 0.004 0.000 0.836
#> SRR1447547 5 0.3023 0.7671 0.000 0.008 0.028 0.096 0.868
#> SRR1354053 4 0.3521 0.3034 0.000 0.232 0.000 0.764 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.1549 0.820 0.936 0.000 0.044 0.020 0.000 0.000
#> SRR1349562 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4955 0.725 0.000 0.084 0.032 0.724 0.148 0.012
#> SRR1499040 5 0.5804 0.629 0.008 0.000 0.208 0.044 0.628 0.112
#> SRR1322312 1 0.2471 0.769 0.888 0.000 0.052 0.056 0.004 0.000
#> SRR1324412 3 0.4147 0.749 0.000 0.000 0.552 0.000 0.012 0.436
#> SRR1100991 3 0.4116 0.737 0.000 0.000 0.572 0.000 0.012 0.416
#> SRR1349479 6 0.4077 0.544 0.000 0.000 0.044 0.212 0.008 0.736
#> SRR1431248 5 0.1442 0.805 0.012 0.000 0.040 0.004 0.944 0.000
#> SRR1405054 3 0.4188 0.208 0.208 0.000 0.736 0.004 0.044 0.008
#> SRR1312266 1 0.4219 0.790 0.760 0.000 0.144 0.080 0.016 0.000
#> SRR1409790 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1352507 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1383763 1 0.2711 0.762 0.872 0.000 0.056 0.068 0.004 0.000
#> SRR1468314 2 0.4610 0.435 0.000 0.664 0.056 0.272 0.008 0.000
#> SRR1473674 2 0.3219 0.669 0.000 0.852 0.060 0.056 0.032 0.000
#> SRR1390499 1 0.0146 0.817 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR821043 4 0.3833 0.572 0.000 0.344 0.008 0.648 0.000 0.000
#> SRR1455653 4 0.3774 0.600 0.000 0.328 0.008 0.664 0.000 0.000
#> SRR1335236 2 0.1116 0.737 0.000 0.960 0.028 0.004 0.000 0.008
#> SRR1095383 2 0.4555 0.381 0.000 0.628 0.036 0.328 0.008 0.000
#> SRR1479489 1 0.5239 0.667 0.560 0.000 0.348 0.084 0.008 0.000
#> SRR1310433 2 0.0458 0.745 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1073435 5 0.5898 0.519 0.000 0.028 0.084 0.048 0.644 0.196
#> SRR659649 6 0.0858 0.739 0.000 0.000 0.028 0.004 0.000 0.968
#> SRR1395999 1 0.7123 0.304 0.396 0.000 0.228 0.088 0.288 0.000
#> SRR1105248 6 0.6655 -0.104 0.000 0.000 0.300 0.312 0.028 0.360
#> SRR1338257 1 0.5692 0.649 0.536 0.000 0.348 0.084 0.032 0.000
#> SRR1499395 6 0.0146 0.767 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1350002 2 0.3542 0.655 0.000 0.832 0.068 0.056 0.044 0.000
#> SRR1489757 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1414637 5 0.1933 0.796 0.012 0.000 0.032 0.032 0.924 0.000
#> SRR1478113 4 0.4170 0.671 0.000 0.016 0.040 0.736 0.208 0.000
#> SRR1322477 5 0.3100 0.782 0.012 0.000 0.128 0.024 0.836 0.000
#> SRR1478789 6 0.2341 0.715 0.000 0.000 0.032 0.012 0.056 0.900
#> SRR1414185 6 0.0146 0.767 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1069141 2 0.0632 0.743 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1376852 5 0.6517 0.312 0.280 0.000 0.136 0.076 0.508 0.000
#> SRR1323491 1 0.3183 0.808 0.828 0.000 0.112 0.060 0.000 0.000
#> SRR1338103 5 0.1262 0.803 0.016 0.000 0.020 0.008 0.956 0.000
#> SRR1472012 5 0.1672 0.806 0.016 0.000 0.048 0.004 0.932 0.000
#> SRR1340325 1 0.5381 0.656 0.548 0.000 0.352 0.088 0.012 0.000
#> SRR1087321 6 0.0436 0.769 0.000 0.000 0.004 0.004 0.004 0.988
#> SRR1488790 1 0.1719 0.820 0.924 0.000 0.060 0.016 0.000 0.000
#> SRR1334866 5 0.1251 0.794 0.000 0.000 0.012 0.008 0.956 0.024
#> SRR1089446 3 0.5458 0.625 0.000 0.000 0.536 0.000 0.144 0.320
#> SRR1344445 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1412969 6 0.0000 0.768 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1071668 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1075804 1 0.4588 0.783 0.744 0.000 0.140 0.072 0.044 0.000
#> SRR1383283 5 0.7319 0.188 0.000 0.052 0.076 0.112 0.464 0.296
#> SRR1350239 3 0.5643 0.608 0.000 0.000 0.556 0.136 0.012 0.296
#> SRR1353878 1 0.5671 0.657 0.544 0.000 0.340 0.084 0.032 0.000
#> SRR1375721 1 0.1334 0.801 0.948 0.000 0.020 0.032 0.000 0.000
#> SRR1083983 5 0.3630 0.766 0.016 0.000 0.136 0.044 0.804 0.000
#> SRR1090095 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.4280 0.673 0.000 0.020 0.040 0.728 0.212 0.000
#> SRR1098737 1 0.4748 0.777 0.728 0.000 0.152 0.076 0.044 0.000
#> SRR1349409 1 0.1334 0.801 0.948 0.000 0.020 0.032 0.000 0.000
#> SRR1413008 3 0.5643 0.608 0.000 0.000 0.556 0.136 0.012 0.296
#> SRR1407179 5 0.4026 0.690 0.000 0.000 0.040 0.036 0.780 0.144
#> SRR1095913 6 0.5395 0.570 0.000 0.124 0.052 0.032 0.080 0.712
#> SRR1403544 1 0.0146 0.816 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1490546 1 0.3912 0.789 0.760 0.000 0.164 0.076 0.000 0.000
#> SRR807971 3 0.4177 0.759 0.000 0.000 0.520 0.000 0.012 0.468
#> SRR1436228 5 0.0653 0.799 0.012 0.000 0.004 0.004 0.980 0.000
#> SRR1445218 2 0.0458 0.745 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1485438 5 0.6052 0.342 0.000 0.320 0.084 0.064 0.532 0.000
#> SRR1358143 1 0.2471 0.769 0.888 0.000 0.052 0.056 0.004 0.000
#> SRR1328760 1 0.5650 0.669 0.552 0.000 0.332 0.084 0.032 0.000
#> SRR1380806 1 0.0146 0.816 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1379426 6 0.0146 0.767 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1087007 6 0.0146 0.769 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1086256 5 0.1642 0.776 0.000 0.004 0.028 0.032 0.936 0.000
#> SRR1346734 4 0.3265 0.721 0.000 0.248 0.004 0.748 0.000 0.000
#> SRR1414515 1 0.1176 0.803 0.956 0.000 0.020 0.024 0.000 0.000
#> SRR1082151 5 0.3806 0.768 0.012 0.000 0.112 0.080 0.796 0.000
#> SRR1349320 4 0.4695 0.734 0.000 0.104 0.032 0.732 0.132 0.000
#> SRR1317554 2 0.4536 0.162 0.000 0.560 0.028 0.408 0.004 0.000
#> SRR1076022 2 0.0260 0.746 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1339573 6 0.0508 0.760 0.000 0.000 0.012 0.004 0.000 0.984
#> SRR1455878 3 0.7206 -0.340 0.284 0.000 0.344 0.084 0.288 0.000
#> SRR1446203 6 0.1562 0.750 0.000 0.000 0.032 0.024 0.004 0.940
#> SRR1387397 5 0.4525 0.708 0.016 0.000 0.180 0.080 0.724 0.000
#> SRR1402590 1 0.0146 0.816 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1317532 1 0.6432 0.593 0.480 0.000 0.340 0.088 0.092 0.000
#> SRR1331488 1 0.3963 0.795 0.756 0.000 0.164 0.080 0.000 0.000
#> SRR1499675 5 0.2145 0.793 0.016 0.000 0.020 0.012 0.920 0.032
#> SRR1440467 6 0.0508 0.766 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR807995 2 0.3607 0.651 0.000 0.828 0.068 0.056 0.048 0.000
#> SRR1476485 4 0.3265 0.721 0.000 0.248 0.004 0.748 0.000 0.000
#> SRR1388214 1 0.5846 0.627 0.512 0.000 0.364 0.084 0.040 0.000
#> SRR1456051 1 0.3159 0.809 0.832 0.000 0.100 0.068 0.000 0.000
#> SRR1473275 3 0.4183 0.743 0.000 0.000 0.508 0.000 0.012 0.480
#> SRR1444083 1 0.5719 0.635 0.524 0.000 0.360 0.084 0.032 0.000
#> SRR1313807 6 0.7807 0.185 0.000 0.212 0.092 0.164 0.076 0.456
#> SRR1470751 5 0.3806 0.768 0.012 0.000 0.112 0.080 0.796 0.000
#> SRR1403434 6 0.0508 0.766 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1390540 1 0.3313 0.807 0.816 0.000 0.124 0.060 0.000 0.000
#> SRR1093861 2 0.0790 0.740 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR1325290 5 0.1738 0.805 0.016 0.000 0.052 0.004 0.928 0.000
#> SRR1070689 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.2649 0.765 0.876 0.000 0.052 0.068 0.004 0.000
#> SRR1081184 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.816 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 5 0.4226 0.625 0.000 0.000 0.032 0.032 0.744 0.192
#> SRR1321877 6 0.0551 0.769 0.000 0.000 0.004 0.008 0.004 0.984
#> SRR815711 3 0.4863 0.727 0.000 0.000 0.528 0.000 0.060 0.412
#> SRR1433476 6 0.5775 0.190 0.000 0.012 0.056 0.388 0.032 0.512
#> SRR1101883 3 0.4175 0.758 0.000 0.000 0.524 0.000 0.012 0.464
#> SRR1433729 2 0.5335 0.340 0.000 0.596 0.084 0.300 0.020 0.000
#> SRR1341877 5 0.1520 0.803 0.016 0.000 0.020 0.008 0.948 0.008
#> SRR1090556 5 0.1932 0.803 0.016 0.000 0.040 0.020 0.924 0.000
#> SRR1357389 3 0.4091 0.752 0.000 0.000 0.520 0.000 0.008 0.472
#> SRR1404227 6 0.5241 0.108 0.000 0.000 0.036 0.032 0.432 0.500
#> SRR1376830 1 0.2688 0.815 0.868 0.000 0.068 0.064 0.000 0.000
#> SRR1500661 1 0.1088 0.819 0.960 0.000 0.024 0.016 0.000 0.000
#> SRR1080294 2 0.4555 0.381 0.000 0.628 0.036 0.328 0.008 0.000
#> SRR1336314 4 0.3276 0.725 0.000 0.228 0.004 0.764 0.004 0.000
#> SRR1102152 1 0.5339 0.662 0.552 0.000 0.352 0.084 0.012 0.000
#> SRR1345244 6 0.0146 0.767 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1478637 5 0.1528 0.793 0.000 0.000 0.016 0.028 0.944 0.012
#> SRR1443776 6 0.0551 0.769 0.000 0.000 0.004 0.008 0.004 0.984
#> SRR1120939 6 0.1642 0.748 0.000 0.000 0.032 0.028 0.004 0.936
#> SRR1080117 6 0.0146 0.767 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1102899 2 0.0458 0.745 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1091865 5 0.4576 0.719 0.020 0.000 0.176 0.080 0.724 0.000
#> SRR1361072 1 0.4634 0.740 0.656 0.000 0.264 0.080 0.000 0.000
#> SRR1487890 1 0.0520 0.813 0.984 0.000 0.008 0.008 0.000 0.000
#> SRR1349456 6 0.5613 0.547 0.000 0.152 0.064 0.032 0.064 0.688
#> SRR1389384 5 0.3710 0.771 0.012 0.000 0.108 0.076 0.804 0.000
#> SRR1316096 2 0.0260 0.746 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1408512 5 0.4395 0.717 0.016 0.000 0.164 0.080 0.740 0.000
#> SRR1447547 5 0.3492 0.715 0.000 0.000 0.076 0.120 0.804 0.000
#> SRR1354053 2 0.4088 0.327 0.000 0.616 0.016 0.368 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.969 0.946 0.978 0.5026 0.498 0.498
#> 3 3 0.949 0.926 0.972 0.3121 0.739 0.526
#> 4 4 0.812 0.701 0.859 0.0998 0.914 0.756
#> 5 5 0.766 0.729 0.784 0.0632 0.885 0.632
#> 6 6 0.808 0.723 0.834 0.0408 0.974 0.888
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.974 1.000 0.000
#> SRR1349562 1 0.0000 0.974 1.000 0.000
#> SRR1353376 2 0.0000 0.980 0.000 1.000
#> SRR1499040 1 0.0000 0.974 1.000 0.000
#> SRR1322312 1 0.0000 0.974 1.000 0.000
#> SRR1324412 1 0.0000 0.974 1.000 0.000
#> SRR1100991 1 0.0000 0.974 1.000 0.000
#> SRR1349479 2 0.0000 0.980 0.000 1.000
#> SRR1431248 2 0.9661 0.348 0.392 0.608
#> SRR1405054 1 0.0000 0.974 1.000 0.000
#> SRR1312266 1 0.0000 0.974 1.000 0.000
#> SRR1409790 1 0.0000 0.974 1.000 0.000
#> SRR1352507 1 0.0000 0.974 1.000 0.000
#> SRR1383763 1 0.0000 0.974 1.000 0.000
#> SRR1468314 2 0.0000 0.980 0.000 1.000
#> SRR1473674 2 0.0000 0.980 0.000 1.000
#> SRR1390499 1 0.0000 0.974 1.000 0.000
#> SRR821043 2 0.0000 0.980 0.000 1.000
#> SRR1455653 2 0.0000 0.980 0.000 1.000
#> SRR1335236 2 0.0000 0.980 0.000 1.000
#> SRR1095383 2 0.0000 0.980 0.000 1.000
#> SRR1479489 1 0.0000 0.974 1.000 0.000
#> SRR1310433 2 0.0000 0.980 0.000 1.000
#> SRR1073435 2 0.0000 0.980 0.000 1.000
#> SRR659649 2 0.0000 0.980 0.000 1.000
#> SRR1395999 1 0.0000 0.974 1.000 0.000
#> SRR1105248 2 0.0000 0.980 0.000 1.000
#> SRR1338257 1 0.0000 0.974 1.000 0.000
#> SRR1499395 2 0.0000 0.980 0.000 1.000
#> SRR1350002 2 0.0000 0.980 0.000 1.000
#> SRR1489757 1 0.0000 0.974 1.000 0.000
#> SRR1414637 1 0.7219 0.752 0.800 0.200
#> SRR1478113 2 0.0000 0.980 0.000 1.000
#> SRR1322477 1 0.0000 0.974 1.000 0.000
#> SRR1478789 2 0.0000 0.980 0.000 1.000
#> SRR1414185 2 0.0000 0.980 0.000 1.000
#> SRR1069141 2 0.0000 0.980 0.000 1.000
#> SRR1376852 1 0.0000 0.974 1.000 0.000
#> SRR1323491 1 0.0000 0.974 1.000 0.000
#> SRR1338103 1 0.4562 0.885 0.904 0.096
#> SRR1472012 1 0.0000 0.974 1.000 0.000
#> SRR1340325 1 0.0000 0.974 1.000 0.000
#> SRR1087321 2 0.0000 0.980 0.000 1.000
#> SRR1488790 1 0.0000 0.974 1.000 0.000
#> SRR1334866 2 0.0000 0.980 0.000 1.000
#> SRR1089446 1 0.8661 0.600 0.712 0.288
#> SRR1344445 1 0.5519 0.847 0.872 0.128
#> SRR1412969 2 0.0000 0.980 0.000 1.000
#> SRR1071668 1 0.0376 0.971 0.996 0.004
#> SRR1075804 1 0.0000 0.974 1.000 0.000
#> SRR1383283 2 0.0000 0.980 0.000 1.000
#> SRR1350239 2 0.0000 0.980 0.000 1.000
#> SRR1353878 1 0.0000 0.974 1.000 0.000
#> SRR1375721 1 0.0000 0.974 1.000 0.000
#> SRR1083983 1 0.0000 0.974 1.000 0.000
#> SRR1090095 1 0.0000 0.974 1.000 0.000
#> SRR1414792 1 0.0000 0.974 1.000 0.000
#> SRR1075102 2 0.0000 0.980 0.000 1.000
#> SRR1098737 1 0.0000 0.974 1.000 0.000
#> SRR1349409 1 0.0000 0.974 1.000 0.000
#> SRR1413008 2 0.0000 0.980 0.000 1.000
#> SRR1407179 2 0.9686 0.337 0.396 0.604
#> SRR1095913 2 0.0000 0.980 0.000 1.000
#> SRR1403544 1 0.0000 0.974 1.000 0.000
#> SRR1490546 1 0.0000 0.974 1.000 0.000
#> SRR807971 1 0.0000 0.974 1.000 0.000
#> SRR1436228 2 0.0000 0.980 0.000 1.000
#> SRR1445218 2 0.0000 0.980 0.000 1.000
#> SRR1485438 2 0.0000 0.980 0.000 1.000
#> SRR1358143 1 0.0000 0.974 1.000 0.000
#> SRR1328760 1 0.0000 0.974 1.000 0.000
#> SRR1380806 1 0.0000 0.974 1.000 0.000
#> SRR1379426 2 0.0000 0.980 0.000 1.000
#> SRR1087007 2 0.0000 0.980 0.000 1.000
#> SRR1086256 2 0.0000 0.980 0.000 1.000
#> SRR1346734 2 0.0000 0.980 0.000 1.000
#> SRR1414515 1 0.0000 0.974 1.000 0.000
#> SRR1082151 1 0.4022 0.901 0.920 0.080
#> SRR1349320 2 0.0000 0.980 0.000 1.000
#> SRR1317554 2 0.0000 0.980 0.000 1.000
#> SRR1076022 2 0.0000 0.980 0.000 1.000
#> SRR1339573 2 0.0000 0.980 0.000 1.000
#> SRR1455878 1 0.0000 0.974 1.000 0.000
#> SRR1446203 2 0.0000 0.980 0.000 1.000
#> SRR1387397 1 0.0000 0.974 1.000 0.000
#> SRR1402590 1 0.0000 0.974 1.000 0.000
#> SRR1317532 1 0.0000 0.974 1.000 0.000
#> SRR1331488 1 0.0000 0.974 1.000 0.000
#> SRR1499675 2 0.9850 0.245 0.428 0.572
#> SRR1440467 2 0.0000 0.980 0.000 1.000
#> SRR807995 2 0.0000 0.980 0.000 1.000
#> SRR1476485 2 0.0000 0.980 0.000 1.000
#> SRR1388214 1 0.0000 0.974 1.000 0.000
#> SRR1456051 1 0.0000 0.974 1.000 0.000
#> SRR1473275 1 0.0376 0.971 0.996 0.004
#> SRR1444083 1 0.0000 0.974 1.000 0.000
#> SRR1313807 2 0.0000 0.980 0.000 1.000
#> SRR1470751 1 0.0000 0.974 1.000 0.000
#> SRR1403434 2 0.0000 0.980 0.000 1.000
#> SRR1390540 1 0.0000 0.974 1.000 0.000
#> SRR1093861 2 0.0000 0.980 0.000 1.000
#> SRR1325290 1 0.0000 0.974 1.000 0.000
#> SRR1070689 1 0.0000 0.974 1.000 0.000
#> SRR1384049 1 0.0000 0.974 1.000 0.000
#> SRR1081184 1 0.0000 0.974 1.000 0.000
#> SRR1324295 1 0.0000 0.974 1.000 0.000
#> SRR1365313 2 0.0000 0.980 0.000 1.000
#> SRR1321877 2 0.0000 0.980 0.000 1.000
#> SRR815711 1 0.8661 0.600 0.712 0.288
#> SRR1433476 2 0.0000 0.980 0.000 1.000
#> SRR1101883 1 0.7602 0.722 0.780 0.220
#> SRR1433729 2 0.0000 0.980 0.000 1.000
#> SRR1341877 1 0.3274 0.921 0.940 0.060
#> SRR1090556 1 0.0000 0.974 1.000 0.000
#> SRR1357389 1 0.9608 0.385 0.616 0.384
#> SRR1404227 2 0.0000 0.980 0.000 1.000
#> SRR1376830 1 0.0000 0.974 1.000 0.000
#> SRR1500661 1 0.0000 0.974 1.000 0.000
#> SRR1080294 2 0.0000 0.980 0.000 1.000
#> SRR1336314 2 0.0000 0.980 0.000 1.000
#> SRR1102152 1 0.0000 0.974 1.000 0.000
#> SRR1345244 2 0.0000 0.980 0.000 1.000
#> SRR1478637 2 0.0000 0.980 0.000 1.000
#> SRR1443776 2 0.0000 0.980 0.000 1.000
#> SRR1120939 2 0.0000 0.980 0.000 1.000
#> SRR1080117 2 0.0000 0.980 0.000 1.000
#> SRR1102899 2 0.0000 0.980 0.000 1.000
#> SRR1091865 1 0.0000 0.974 1.000 0.000
#> SRR1361072 1 0.0000 0.974 1.000 0.000
#> SRR1487890 1 0.0000 0.974 1.000 0.000
#> SRR1349456 2 0.0000 0.980 0.000 1.000
#> SRR1389384 1 0.0000 0.974 1.000 0.000
#> SRR1316096 2 0.0000 0.980 0.000 1.000
#> SRR1408512 1 0.0000 0.974 1.000 0.000
#> SRR1447547 2 0.0000 0.980 0.000 1.000
#> SRR1354053 2 0.0000 0.980 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1499040 1 0.4504 0.7473 0.804 0.000 0.196
#> SRR1322312 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1349479 3 0.0237 0.9593 0.000 0.004 0.996
#> SRR1431248 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1405054 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1312266 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1335236 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1095383 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1073435 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR659649 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1105248 3 0.6079 0.3607 0.000 0.388 0.612
#> SRR1338257 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1414637 2 0.0592 0.9493 0.012 0.988 0.000
#> SRR1478113 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1478789 3 0.6235 0.2392 0.000 0.436 0.564
#> SRR1414185 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1338103 1 0.4555 0.7405 0.800 0.200 0.000
#> SRR1472012 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1334866 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1089446 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1383283 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1350239 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1353878 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1413008 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1407179 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1095913 2 0.4235 0.7593 0.000 0.824 0.176
#> SRR1403544 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1436228 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1445218 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1082151 2 0.5706 0.5194 0.320 0.680 0.000
#> SRR1349320 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1499675 1 0.6280 0.1344 0.540 0.460 0.000
#> SRR1440467 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1313807 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1470751 2 0.6305 0.0535 0.484 0.516 0.000
#> SRR1403434 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1365313 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1321877 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1433476 2 0.5216 0.6211 0.000 0.740 0.260
#> SRR1101883 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1341877 1 0.2625 0.8933 0.916 0.084 0.000
#> SRR1090556 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1404227 3 0.6168 0.3075 0.000 0.412 0.588
#> SRR1376830 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1478637 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1443776 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.9629 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1349456 2 0.4178 0.7651 0.000 0.828 0.172
#> SRR1389384 1 0.4931 0.6900 0.768 0.232 0.000
#> SRR1316096 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.9779 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.9611 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.9611 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1499040 2 0.7846 0.0883 0.272 0.392 0.336 0.000
#> SRR1322312 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1349479 4 0.6404 0.2135 0.000 0.096 0.296 0.608
#> SRR1431248 4 0.4948 0.0705 0.000 0.440 0.000 0.560
#> SRR1405054 1 0.2281 0.8466 0.904 0.000 0.096 0.000
#> SRR1312266 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1409790 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.1118 0.6926 0.000 0.036 0.000 0.964
#> SRR1473674 4 0.4999 0.0351 0.000 0.492 0.000 0.508
#> SRR1390499 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1335236 4 0.4999 0.0351 0.000 0.492 0.000 0.508
#> SRR1095383 4 0.0817 0.6981 0.000 0.024 0.000 0.976
#> SRR1479489 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1310433 4 0.4989 0.0809 0.000 0.472 0.000 0.528
#> SRR1073435 4 0.1211 0.6932 0.000 0.040 0.000 0.960
#> SRR659649 3 0.0921 0.9105 0.000 0.028 0.972 0.000
#> SRR1395999 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.3801 0.4696 0.000 0.000 0.220 0.780
#> SRR1338257 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1350002 2 0.4992 -0.0128 0.000 0.524 0.000 0.476
#> SRR1489757 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1414637 2 0.2345 0.5695 0.000 0.900 0.000 0.100
#> SRR1478113 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1322477 1 0.4898 0.4431 0.584 0.416 0.000 0.000
#> SRR1478789 2 0.7080 0.3016 0.000 0.568 0.236 0.196
#> SRR1414185 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1069141 4 0.4998 0.0455 0.000 0.488 0.000 0.512
#> SRR1376852 1 0.2868 0.8233 0.864 0.136 0.000 0.000
#> SRR1323491 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.6575 0.2695 0.508 0.412 0.000 0.080
#> SRR1472012 1 0.4888 0.4548 0.588 0.412 0.000 0.000
#> SRR1340325 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1488790 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.0188 0.5523 0.000 0.996 0.000 0.004
#> SRR1089446 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1071668 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1383283 4 0.1022 0.6945 0.000 0.032 0.000 0.968
#> SRR1350239 3 0.5000 0.1160 0.000 0.000 0.504 0.496
#> SRR1353878 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.4406 0.6336 0.700 0.300 0.000 0.000
#> SRR1090095 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1413008 3 0.5000 0.1160 0.000 0.000 0.504 0.496
#> SRR1407179 3 0.2973 0.8744 0.000 0.144 0.856 0.000
#> SRR1095913 4 0.5776 0.0203 0.000 0.468 0.028 0.504
#> SRR1403544 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.2281 0.5703 0.000 0.904 0.000 0.096
#> SRR1445218 4 0.4989 0.0809 0.000 0.472 0.000 0.528
#> SRR1485438 2 0.2408 0.5679 0.000 0.896 0.000 0.104
#> SRR1358143 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1087007 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1086256 2 0.4994 -0.0253 0.000 0.520 0.000 0.480
#> SRR1346734 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.3525 0.5647 0.040 0.860 0.000 0.100
#> SRR1349320 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.0469 0.7005 0.000 0.012 0.000 0.988
#> SRR1076022 4 0.4999 0.0351 0.000 0.492 0.000 0.508
#> SRR1339573 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1455878 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1387397 1 0.0592 0.9262 0.984 0.016 0.000 0.000
#> SRR1402590 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1499675 2 0.7971 0.1305 0.364 0.380 0.004 0.252
#> SRR1440467 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR807995 2 0.4925 0.1259 0.000 0.572 0.000 0.428
#> SRR1476485 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1313807 4 0.2530 0.6320 0.000 0.112 0.000 0.888
#> SRR1470751 2 0.3611 0.5537 0.080 0.860 0.000 0.060
#> SRR1403434 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1390540 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1093861 4 0.4999 0.0351 0.000 0.492 0.000 0.508
#> SRR1325290 1 0.4888 0.4548 0.588 0.412 0.000 0.000
#> SRR1070689 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.4543 0.2587 0.000 0.676 0.000 0.324
#> SRR1321877 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR815711 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1433476 4 0.3716 0.5615 0.000 0.096 0.052 0.852
#> SRR1101883 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1433729 4 0.0817 0.6981 0.000 0.024 0.000 0.976
#> SRR1341877 1 0.4624 0.5850 0.660 0.340 0.000 0.000
#> SRR1090556 1 0.3726 0.7461 0.788 0.212 0.000 0.000
#> SRR1357389 3 0.0000 0.9079 0.000 0.000 1.000 0.000
#> SRR1404227 2 0.7238 0.2708 0.000 0.508 0.332 0.160
#> SRR1376830 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1080294 4 0.0817 0.6981 0.000 0.024 0.000 0.976
#> SRR1336314 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1478637 2 0.2281 0.5703 0.000 0.904 0.000 0.096
#> SRR1443776 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1120939 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1080117 3 0.2281 0.9134 0.000 0.096 0.904 0.000
#> SRR1102899 4 0.4989 0.0809 0.000 0.472 0.000 0.528
#> SRR1091865 1 0.4605 0.5805 0.664 0.336 0.000 0.000
#> SRR1361072 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9374 1.000 0.000 0.000 0.000
#> SRR1349456 2 0.5691 0.0718 0.000 0.564 0.028 0.408
#> SRR1389384 2 0.2921 0.5215 0.140 0.860 0.000 0.000
#> SRR1316096 4 0.4998 0.0455 0.000 0.488 0.000 0.512
#> SRR1408512 1 0.1118 0.9114 0.964 0.036 0.000 0.000
#> SRR1447547 4 0.0000 0.7009 0.000 0.000 0.000 1.000
#> SRR1354053 4 0.0469 0.7005 0.000 0.012 0.000 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 2 0.4682 0.7381 0.000 0.620 0.000 0.356 0.024
#> SRR1499040 5 0.6891 0.4968 0.208 0.000 0.176 0.052 0.564
#> SRR1322312 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1100991 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1349479 3 0.6041 0.1800 0.000 0.128 0.516 0.356 0.000
#> SRR1431248 5 0.3112 0.6209 0.000 0.100 0.000 0.044 0.856
#> SRR1405054 4 0.4171 0.2142 0.396 0.000 0.000 0.604 0.000
#> SRR1312266 1 0.0290 0.9386 0.992 0.000 0.000 0.000 0.008
#> SRR1409790 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1352507 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1383763 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1468314 2 0.3424 0.7591 0.000 0.760 0.000 0.240 0.000
#> SRR1473674 2 0.2629 0.5986 0.000 0.860 0.000 0.004 0.136
#> SRR1390499 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR821043 2 0.4511 0.7389 0.000 0.628 0.000 0.356 0.016
#> SRR1455653 2 0.4654 0.7405 0.000 0.628 0.000 0.348 0.024
#> SRR1335236 2 0.2905 0.6053 0.000 0.868 0.036 0.000 0.096
#> SRR1095383 2 0.3452 0.7593 0.000 0.756 0.000 0.244 0.000
#> SRR1479489 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.1792 0.6430 0.000 0.916 0.000 0.000 0.084
#> SRR1073435 2 0.4933 0.7190 0.000 0.688 0.000 0.236 0.076
#> SRR659649 3 0.2329 0.5469 0.000 0.000 0.876 0.124 0.000
#> SRR1395999 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1105248 2 0.5464 0.6121 0.000 0.476 0.036 0.476 0.012
#> SRR1338257 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1499395 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 2 0.3160 0.5334 0.000 0.808 0.000 0.004 0.188
#> SRR1489757 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1414637 5 0.2338 0.6515 0.000 0.112 0.000 0.004 0.884
#> SRR1478113 2 0.4696 0.7362 0.000 0.616 0.000 0.360 0.024
#> SRR1322477 5 0.3838 0.5971 0.280 0.000 0.000 0.004 0.716
#> SRR1478789 3 0.4414 0.4311 0.000 0.376 0.616 0.004 0.004
#> SRR1414185 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 2 0.1792 0.6430 0.000 0.916 0.000 0.000 0.084
#> SRR1376852 1 0.4251 0.3568 0.624 0.000 0.000 0.004 0.372
#> SRR1323491 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1338103 5 0.3891 0.6730 0.136 0.016 0.000 0.036 0.812
#> SRR1472012 5 0.3141 0.6731 0.152 0.000 0.000 0.016 0.832
#> SRR1340325 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.5575 0.5458 0.000 0.196 0.132 0.008 0.664
#> SRR1089446 4 0.4505 0.8244 0.000 0.000 0.384 0.604 0.012
#> SRR1344445 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1412969 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1071668 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1075804 1 0.2763 0.7894 0.848 0.000 0.000 0.004 0.148
#> SRR1383283 2 0.3934 0.7557 0.000 0.740 0.000 0.244 0.016
#> SRR1350239 4 0.2688 0.3529 0.000 0.056 0.036 0.896 0.012
#> SRR1353878 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1375721 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.4268 0.0097 0.556 0.000 0.000 0.000 0.444
#> SRR1090095 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 2 0.4696 0.7362 0.000 0.616 0.000 0.360 0.024
#> SRR1098737 1 0.2806 0.7843 0.844 0.000 0.000 0.004 0.152
#> SRR1349409 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.2688 0.3529 0.000 0.056 0.036 0.896 0.012
#> SRR1407179 3 0.7411 0.2326 0.000 0.056 0.444 0.176 0.324
#> SRR1095913 2 0.3106 0.5647 0.000 0.844 0.132 0.000 0.024
#> SRR1403544 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR807971 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1436228 5 0.2628 0.6530 0.000 0.088 0.000 0.028 0.884
#> SRR1445218 2 0.1792 0.6430 0.000 0.916 0.000 0.000 0.084
#> SRR1485438 5 0.4166 0.5084 0.000 0.348 0.000 0.004 0.648
#> SRR1358143 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1380806 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 2 0.3123 0.5970 0.000 0.812 0.000 0.004 0.184
#> SRR1346734 2 0.4682 0.7381 0.000 0.620 0.000 0.356 0.024
#> SRR1414515 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.4975 0.5880 0.040 0.272 0.000 0.012 0.676
#> SRR1349320 2 0.4682 0.7381 0.000 0.620 0.000 0.356 0.024
#> SRR1317554 2 0.3835 0.7590 0.000 0.732 0.000 0.260 0.008
#> SRR1076022 2 0.1965 0.6340 0.000 0.904 0.000 0.000 0.096
#> SRR1339573 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 1 0.3231 0.7316 0.800 0.000 0.000 0.004 0.196
#> SRR1446203 3 0.0162 0.7636 0.000 0.004 0.996 0.000 0.000
#> SRR1387397 1 0.4086 0.5719 0.704 0.000 0.000 0.012 0.284
#> SRR1402590 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.2763 0.7894 0.848 0.000 0.000 0.004 0.148
#> SRR1331488 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1499675 5 0.7549 0.4808 0.108 0.088 0.188 0.040 0.576
#> SRR1440467 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR807995 2 0.3266 0.5147 0.000 0.796 0.000 0.004 0.200
#> SRR1476485 2 0.4682 0.7381 0.000 0.620 0.000 0.356 0.024
#> SRR1388214 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1456051 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 4 0.4210 0.8130 0.000 0.000 0.412 0.588 0.000
#> SRR1444083 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1313807 2 0.4589 0.7437 0.000 0.704 0.048 0.248 0.000
#> SRR1470751 5 0.4990 0.6109 0.056 0.248 0.000 0.008 0.688
#> SRR1403434 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1390540 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.1965 0.6340 0.000 0.904 0.000 0.000 0.096
#> SRR1325290 5 0.2843 0.6762 0.144 0.000 0.000 0.008 0.848
#> SRR1070689 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.7171 0.1017 0.000 0.388 0.388 0.028 0.196
#> SRR1321877 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR815711 4 0.4310 0.8328 0.000 0.000 0.392 0.604 0.004
#> SRR1433476 2 0.6906 0.5020 0.000 0.404 0.232 0.356 0.008
#> SRR1101883 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1433729 2 0.3452 0.7593 0.000 0.756 0.000 0.244 0.000
#> SRR1341877 5 0.5848 0.5098 0.308 0.008 0.036 0.036 0.612
#> SRR1090556 5 0.4934 0.4004 0.364 0.000 0.000 0.036 0.600
#> SRR1357389 4 0.4171 0.8361 0.000 0.000 0.396 0.604 0.000
#> SRR1404227 3 0.6537 0.3668 0.000 0.304 0.544 0.028 0.124
#> SRR1376830 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1080294 2 0.3452 0.7593 0.000 0.756 0.000 0.244 0.000
#> SRR1336314 2 0.4682 0.7381 0.000 0.620 0.000 0.356 0.024
#> SRR1102152 1 0.0404 0.9363 0.988 0.000 0.000 0.000 0.012
#> SRR1345244 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 5 0.4040 0.5780 0.000 0.276 0.000 0.012 0.712
#> SRR1443776 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 3 0.0609 0.7517 0.000 0.020 0.980 0.000 0.000
#> SRR1080117 3 0.0000 0.7667 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.1792 0.6430 0.000 0.916 0.000 0.000 0.084
#> SRR1091865 5 0.4452 0.1722 0.496 0.000 0.000 0.004 0.500
#> SRR1361072 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9434 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.4367 0.3918 0.000 0.416 0.580 0.004 0.000
#> SRR1389384 5 0.5282 0.6337 0.100 0.220 0.000 0.004 0.676
#> SRR1316096 2 0.1851 0.6402 0.000 0.912 0.000 0.000 0.088
#> SRR1408512 1 0.3838 0.5944 0.716 0.000 0.000 0.004 0.280
#> SRR1447547 2 0.4696 0.7362 0.000 0.616 0.000 0.360 0.024
#> SRR1354053 2 0.3835 0.7590 0.000 0.732 0.000 0.260 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0260 0.9263 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1349562 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1499040 2 0.4225 0.5589 0.140 0.768 0.060 0.000 0.000 0.032
#> SRR1322312 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1100991 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1349479 6 0.5227 0.4472 0.000 0.000 0.092 0.256 0.020 0.632
#> SRR1431248 5 0.4252 0.4181 0.000 0.312 0.000 0.036 0.652 0.000
#> SRR1405054 3 0.2234 0.7155 0.124 0.000 0.872 0.000 0.004 0.000
#> SRR1312266 1 0.1257 0.9133 0.952 0.020 0.000 0.000 0.028 0.000
#> SRR1409790 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1352507 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1383763 1 0.0363 0.9215 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1468314 4 0.0146 0.7137 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1473674 4 0.4616 0.4200 0.000 0.384 0.036 0.576 0.004 0.000
#> SRR1390499 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.4321 0.6626 0.000 0.008 0.092 0.740 0.160 0.000
#> SRR1455653 4 0.4119 0.6692 0.000 0.008 0.084 0.760 0.148 0.000
#> SRR1335236 4 0.5187 0.5810 0.000 0.200 0.036 0.684 0.008 0.072
#> SRR1095383 4 0.0146 0.7132 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1479489 1 0.1082 0.9152 0.956 0.004 0.000 0.000 0.040 0.000
#> SRR1310433 4 0.3769 0.6433 0.000 0.188 0.036 0.768 0.008 0.000
#> SRR1073435 4 0.3634 0.4455 0.000 0.000 0.008 0.696 0.296 0.000
#> SRR659649 6 0.1610 0.8189 0.000 0.000 0.084 0.000 0.000 0.916
#> SRR1395999 1 0.1049 0.9170 0.960 0.008 0.000 0.000 0.032 0.000
#> SRR1105248 4 0.6266 0.5443 0.000 0.020 0.184 0.584 0.180 0.032
#> SRR1338257 1 0.1720 0.9003 0.928 0.032 0.000 0.000 0.040 0.000
#> SRR1499395 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1350002 4 0.4682 0.3641 0.000 0.420 0.036 0.540 0.004 0.000
#> SRR1489757 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1414637 2 0.2489 0.6176 0.000 0.860 0.000 0.012 0.128 0.000
#> SRR1478113 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1322477 2 0.3790 0.5485 0.104 0.780 0.000 0.000 0.116 0.000
#> SRR1478789 6 0.2509 0.7736 0.000 0.000 0.036 0.088 0.000 0.876
#> SRR1414185 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1069141 4 0.3860 0.6353 0.000 0.200 0.036 0.756 0.008 0.000
#> SRR1376852 1 0.4806 0.1997 0.560 0.060 0.000 0.000 0.380 0.000
#> SRR1323491 1 0.0260 0.9263 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1338103 5 0.3590 0.5688 0.032 0.188 0.000 0.004 0.776 0.000
#> SRR1472012 5 0.3871 0.4421 0.016 0.308 0.000 0.000 0.676 0.000
#> SRR1340325 1 0.0937 0.9165 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR1087321 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 1 0.0146 0.9269 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1334866 2 0.4722 0.5077 0.000 0.720 0.000 0.024 0.156 0.100
#> SRR1089446 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1344445 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1412969 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1071668 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1075804 1 0.3198 0.6258 0.740 0.000 0.000 0.000 0.260 0.000
#> SRR1383283 4 0.1082 0.7053 0.000 0.000 0.004 0.956 0.040 0.000
#> SRR1350239 3 0.5536 0.3869 0.000 0.020 0.620 0.184 0.176 0.000
#> SRR1353878 1 0.1720 0.9003 0.928 0.032 0.000 0.000 0.040 0.000
#> SRR1375721 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 2 0.5560 0.1760 0.384 0.476 0.000 0.000 0.140 0.000
#> SRR1090095 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1098737 1 0.3244 0.6121 0.732 0.000 0.000 0.000 0.268 0.000
#> SRR1349409 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 3 0.5536 0.3869 0.000 0.020 0.620 0.184 0.176 0.000
#> SRR1407179 5 0.4048 0.5088 0.000 0.000 0.188 0.024 0.756 0.032
#> SRR1095913 4 0.5308 0.4471 0.000 0.040 0.036 0.628 0.012 0.284
#> SRR1403544 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0937 0.9165 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR807971 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1436228 5 0.4185 0.3966 0.000 0.332 0.004 0.020 0.644 0.000
#> SRR1445218 4 0.3769 0.6433 0.000 0.188 0.036 0.768 0.008 0.000
#> SRR1485438 2 0.2164 0.6101 0.000 0.908 0.028 0.056 0.008 0.000
#> SRR1358143 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.1720 0.9003 0.928 0.032 0.000 0.000 0.040 0.000
#> SRR1380806 1 0.0146 0.9269 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1379426 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1087007 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1086256 4 0.5101 0.5642 0.000 0.200 0.024 0.672 0.104 0.000
#> SRR1346734 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1414515 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.0291 0.6661 0.004 0.992 0.000 0.000 0.004 0.000
#> SRR1349320 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1317554 4 0.0717 0.7137 0.000 0.000 0.016 0.976 0.008 0.000
#> SRR1076022 4 0.3860 0.6353 0.000 0.200 0.036 0.756 0.008 0.000
#> SRR1339573 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1455878 1 0.4263 0.4203 0.600 0.024 0.000 0.000 0.376 0.000
#> SRR1446203 6 0.0692 0.8885 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR1387397 5 0.4353 0.2405 0.384 0.028 0.000 0.000 0.588 0.000
#> SRR1402590 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.3330 0.6327 0.716 0.000 0.000 0.000 0.284 0.000
#> SRR1331488 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1499675 5 0.4399 0.5801 0.016 0.084 0.004 0.056 0.792 0.048
#> SRR1440467 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR807995 4 0.4771 0.3786 0.000 0.412 0.036 0.544 0.008 0.000
#> SRR1476485 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1388214 1 0.1720 0.9003 0.928 0.032 0.000 0.000 0.040 0.000
#> SRR1456051 1 0.0865 0.9180 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1473275 3 0.2219 0.9026 0.000 0.000 0.864 0.000 0.000 0.136
#> SRR1444083 1 0.1720 0.9003 0.928 0.032 0.000 0.000 0.040 0.000
#> SRR1313807 4 0.1418 0.6987 0.000 0.000 0.000 0.944 0.032 0.024
#> SRR1470751 2 0.0291 0.6661 0.004 0.992 0.000 0.000 0.004 0.000
#> SRR1403434 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1390540 1 0.0260 0.9263 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1093861 4 0.3860 0.6353 0.000 0.200 0.036 0.756 0.008 0.000
#> SRR1325290 2 0.4258 -0.0726 0.016 0.516 0.000 0.000 0.468 0.000
#> SRR1070689 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0260 0.9237 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1081184 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 5 0.8035 0.0751 0.000 0.188 0.036 0.264 0.360 0.152
#> SRR1321877 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1433476 4 0.7033 0.4268 0.000 0.012 0.092 0.496 0.172 0.228
#> SRR1101883 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1433729 4 0.0291 0.7134 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1341877 5 0.4045 0.5739 0.136 0.076 0.000 0.004 0.776 0.008
#> SRR1090556 5 0.3293 0.5460 0.140 0.048 0.000 0.000 0.812 0.000
#> SRR1357389 3 0.2135 0.9105 0.000 0.000 0.872 0.000 0.000 0.128
#> SRR1404227 6 0.6634 0.0395 0.000 0.000 0.036 0.232 0.320 0.412
#> SRR1376830 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0458 0.9197 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1080294 4 0.0146 0.7132 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1336314 4 0.4749 0.6514 0.000 0.020 0.092 0.708 0.180 0.000
#> SRR1102152 1 0.1644 0.9029 0.932 0.028 0.000 0.000 0.040 0.000
#> SRR1345244 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1478637 2 0.3123 0.5863 0.000 0.832 0.000 0.056 0.112 0.000
#> SRR1443776 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 6 0.1053 0.8815 0.000 0.000 0.004 0.012 0.020 0.964
#> SRR1080117 6 0.0000 0.9019 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1102899 4 0.3737 0.6453 0.000 0.184 0.036 0.772 0.008 0.000
#> SRR1091865 2 0.3989 0.4614 0.236 0.720 0.000 0.000 0.044 0.000
#> SRR1361072 1 0.0937 0.9165 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR1487890 1 0.0000 0.9272 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.4897 0.4605 0.000 0.000 0.036 0.312 0.028 0.624
#> SRR1389384 2 0.0692 0.6682 0.020 0.976 0.000 0.000 0.004 0.000
#> SRR1316096 4 0.3800 0.6407 0.000 0.192 0.036 0.764 0.008 0.000
#> SRR1408512 1 0.4555 0.2550 0.540 0.036 0.000 0.000 0.424 0.000
#> SRR1447547 4 0.4779 0.6488 0.000 0.020 0.092 0.704 0.184 0.000
#> SRR1354053 4 0.0717 0.7137 0.000 0.000 0.016 0.976 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.895 0.923 0.968 0.4895 0.505 0.505
#> 3 3 0.827 0.840 0.940 0.3151 0.775 0.586
#> 4 4 0.725 0.697 0.860 0.1533 0.858 0.623
#> 5 5 0.810 0.806 0.911 0.0729 0.860 0.532
#> 6 6 0.821 0.792 0.903 0.0250 0.977 0.888
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.946 1.000 0.000
#> SRR1349562 1 0.0000 0.946 1.000 0.000
#> SRR1353376 2 0.0376 0.980 0.004 0.996
#> SRR1499040 1 0.9977 0.160 0.528 0.472
#> SRR1322312 1 0.0000 0.946 1.000 0.000
#> SRR1324412 2 0.8763 0.559 0.296 0.704
#> SRR1100991 1 0.9983 0.140 0.524 0.476
#> SRR1349479 2 0.0000 0.981 0.000 1.000
#> SRR1431248 2 0.8861 0.544 0.304 0.696
#> SRR1405054 1 0.0000 0.946 1.000 0.000
#> SRR1312266 1 0.0000 0.946 1.000 0.000
#> SRR1409790 2 0.0938 0.974 0.012 0.988
#> SRR1352507 2 0.0376 0.980 0.004 0.996
#> SRR1383763 1 0.0000 0.946 1.000 0.000
#> SRR1468314 2 0.0000 0.981 0.000 1.000
#> SRR1473674 2 0.0000 0.981 0.000 1.000
#> SRR1390499 1 0.0000 0.946 1.000 0.000
#> SRR821043 2 0.0000 0.981 0.000 1.000
#> SRR1455653 2 0.0000 0.981 0.000 1.000
#> SRR1335236 2 0.0000 0.981 0.000 1.000
#> SRR1095383 2 0.0000 0.981 0.000 1.000
#> SRR1479489 1 0.0000 0.946 1.000 0.000
#> SRR1310433 2 0.0000 0.981 0.000 1.000
#> SRR1073435 2 0.0376 0.980 0.004 0.996
#> SRR659649 2 0.0000 0.981 0.000 1.000
#> SRR1395999 1 0.0000 0.946 1.000 0.000
#> SRR1105248 2 0.0376 0.980 0.004 0.996
#> SRR1338257 1 0.0000 0.946 1.000 0.000
#> SRR1499395 2 0.0000 0.981 0.000 1.000
#> SRR1350002 2 0.0672 0.976 0.008 0.992
#> SRR1489757 2 0.0376 0.980 0.004 0.996
#> SRR1414637 1 0.8955 0.566 0.688 0.312
#> SRR1478113 2 0.7602 0.708 0.220 0.780
#> SRR1322477 1 0.0000 0.946 1.000 0.000
#> SRR1478789 2 0.0000 0.981 0.000 1.000
#> SRR1414185 2 0.0000 0.981 0.000 1.000
#> SRR1069141 2 0.0000 0.981 0.000 1.000
#> SRR1376852 1 0.0000 0.946 1.000 0.000
#> SRR1323491 1 0.0000 0.946 1.000 0.000
#> SRR1338103 1 0.7815 0.698 0.768 0.232
#> SRR1472012 1 0.5519 0.829 0.872 0.128
#> SRR1340325 1 0.0000 0.946 1.000 0.000
#> SRR1087321 2 0.0000 0.981 0.000 1.000
#> SRR1488790 1 0.0000 0.946 1.000 0.000
#> SRR1334866 2 0.0000 0.981 0.000 1.000
#> SRR1089446 2 0.0376 0.980 0.004 0.996
#> SRR1344445 2 0.0376 0.980 0.004 0.996
#> SRR1412969 2 0.0000 0.981 0.000 1.000
#> SRR1071668 2 0.0376 0.980 0.004 0.996
#> SRR1075804 1 0.0000 0.946 1.000 0.000
#> SRR1383283 2 0.0000 0.981 0.000 1.000
#> SRR1350239 2 0.0938 0.974 0.012 0.988
#> SRR1353878 1 0.0000 0.946 1.000 0.000
#> SRR1375721 1 0.0000 0.946 1.000 0.000
#> SRR1083983 1 0.0000 0.946 1.000 0.000
#> SRR1090095 1 0.0000 0.946 1.000 0.000
#> SRR1414792 1 0.0000 0.946 1.000 0.000
#> SRR1075102 2 0.3274 0.927 0.060 0.940
#> SRR1098737 1 0.0000 0.946 1.000 0.000
#> SRR1349409 1 0.0000 0.946 1.000 0.000
#> SRR1413008 2 0.0938 0.974 0.012 0.988
#> SRR1407179 2 0.0938 0.974 0.012 0.988
#> SRR1095913 2 0.0000 0.981 0.000 1.000
#> SRR1403544 1 0.0000 0.946 1.000 0.000
#> SRR1490546 1 0.0000 0.946 1.000 0.000
#> SRR807971 2 0.0376 0.980 0.004 0.996
#> SRR1436228 2 0.6247 0.806 0.156 0.844
#> SRR1445218 2 0.0000 0.981 0.000 1.000
#> SRR1485438 2 0.0672 0.976 0.008 0.992
#> SRR1358143 1 0.0000 0.946 1.000 0.000
#> SRR1328760 1 0.0000 0.946 1.000 0.000
#> SRR1380806 1 0.0000 0.946 1.000 0.000
#> SRR1379426 2 0.0000 0.981 0.000 1.000
#> SRR1087007 2 0.0000 0.981 0.000 1.000
#> SRR1086256 2 0.0000 0.981 0.000 1.000
#> SRR1346734 2 0.0000 0.981 0.000 1.000
#> SRR1414515 1 0.0000 0.946 1.000 0.000
#> SRR1082151 1 0.9427 0.460 0.640 0.360
#> SRR1349320 2 0.0000 0.981 0.000 1.000
#> SRR1317554 2 0.0000 0.981 0.000 1.000
#> SRR1076022 2 0.0000 0.981 0.000 1.000
#> SRR1339573 2 0.0000 0.981 0.000 1.000
#> SRR1455878 1 0.0000 0.946 1.000 0.000
#> SRR1446203 2 0.0000 0.981 0.000 1.000
#> SRR1387397 1 0.0000 0.946 1.000 0.000
#> SRR1402590 1 0.0000 0.946 1.000 0.000
#> SRR1317532 1 0.0000 0.946 1.000 0.000
#> SRR1331488 1 0.0000 0.946 1.000 0.000
#> SRR1499675 2 0.0376 0.980 0.004 0.996
#> SRR1440467 2 0.0000 0.981 0.000 1.000
#> SRR807995 2 0.0000 0.981 0.000 1.000
#> SRR1476485 2 0.0000 0.981 0.000 1.000
#> SRR1388214 1 0.0000 0.946 1.000 0.000
#> SRR1456051 1 0.0000 0.946 1.000 0.000
#> SRR1473275 2 0.0000 0.981 0.000 1.000
#> SRR1444083 1 0.0000 0.946 1.000 0.000
#> SRR1313807 2 0.0000 0.981 0.000 1.000
#> SRR1470751 1 0.0938 0.937 0.988 0.012
#> SRR1403434 2 0.0000 0.981 0.000 1.000
#> SRR1390540 1 0.0000 0.946 1.000 0.000
#> SRR1093861 2 0.0000 0.981 0.000 1.000
#> SRR1325290 1 0.6531 0.784 0.832 0.168
#> SRR1070689 1 0.0000 0.946 1.000 0.000
#> SRR1384049 1 0.0000 0.946 1.000 0.000
#> SRR1081184 1 0.0000 0.946 1.000 0.000
#> SRR1324295 1 0.0000 0.946 1.000 0.000
#> SRR1365313 2 0.0000 0.981 0.000 1.000
#> SRR1321877 2 0.0000 0.981 0.000 1.000
#> SRR815711 2 0.0376 0.980 0.004 0.996
#> SRR1433476 2 0.0376 0.980 0.004 0.996
#> SRR1101883 2 0.0376 0.980 0.004 0.996
#> SRR1433729 2 0.0000 0.981 0.000 1.000
#> SRR1341877 2 0.5408 0.852 0.124 0.876
#> SRR1090556 1 0.9977 0.156 0.528 0.472
#> SRR1357389 2 0.0376 0.980 0.004 0.996
#> SRR1404227 2 0.0000 0.981 0.000 1.000
#> SRR1376830 1 0.0000 0.946 1.000 0.000
#> SRR1500661 1 0.0000 0.946 1.000 0.000
#> SRR1080294 2 0.0000 0.981 0.000 1.000
#> SRR1336314 2 0.0938 0.974 0.012 0.988
#> SRR1102152 1 0.0000 0.946 1.000 0.000
#> SRR1345244 2 0.0000 0.981 0.000 1.000
#> SRR1478637 2 0.0000 0.981 0.000 1.000
#> SRR1443776 2 0.0000 0.981 0.000 1.000
#> SRR1120939 2 0.0000 0.981 0.000 1.000
#> SRR1080117 2 0.0000 0.981 0.000 1.000
#> SRR1102899 2 0.0000 0.981 0.000 1.000
#> SRR1091865 1 0.0000 0.946 1.000 0.000
#> SRR1361072 1 0.0000 0.946 1.000 0.000
#> SRR1487890 1 0.0000 0.946 1.000 0.000
#> SRR1349456 2 0.0000 0.981 0.000 1.000
#> SRR1389384 1 0.9393 0.468 0.644 0.356
#> SRR1316096 2 0.0000 0.981 0.000 1.000
#> SRR1408512 1 0.0376 0.943 0.996 0.004
#> SRR1447547 2 0.2603 0.944 0.044 0.956
#> SRR1354053 2 0.0000 0.981 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1353376 2 0.3752 0.83273 0.000 0.856 0.144
#> SRR1499040 3 0.6104 0.42586 0.348 0.004 0.648
#> SRR1322312 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1324412 3 0.0237 0.90098 0.004 0.000 0.996
#> SRR1100991 3 0.0237 0.90098 0.004 0.000 0.996
#> SRR1349479 3 0.3482 0.79704 0.000 0.128 0.872
#> SRR1431248 3 0.1989 0.86870 0.048 0.004 0.948
#> SRR1405054 1 0.6307 0.00867 0.512 0.000 0.488
#> SRR1312266 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1409790 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1335236 2 0.4504 0.76382 0.000 0.804 0.196
#> SRR1095383 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1073435 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR659649 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1105248 3 0.6309 0.03969 0.000 0.496 0.504
#> SRR1338257 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1414637 3 0.6680 0.05937 0.484 0.008 0.508
#> SRR1478113 2 0.1411 0.94433 0.000 0.964 0.036
#> SRR1322477 1 0.0237 0.93407 0.996 0.004 0.000
#> SRR1478789 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1414185 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1338103 3 0.6518 0.06570 0.484 0.004 0.512
#> SRR1472012 3 0.6489 0.16187 0.456 0.004 0.540
#> SRR1340325 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1334866 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1089446 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1344445 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1412969 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1071668 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1383283 3 0.6111 0.35288 0.000 0.396 0.604
#> SRR1350239 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1353878 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1083983 1 0.0592 0.92761 0.988 0.000 0.012
#> SRR1090095 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1075102 2 0.4178 0.79332 0.000 0.828 0.172
#> SRR1098737 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1413008 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1407179 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1095913 3 0.0424 0.89980 0.000 0.008 0.992
#> SRR1403544 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1436228 3 0.1765 0.87509 0.040 0.004 0.956
#> SRR1445218 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1485438 3 0.4002 0.75146 0.000 0.160 0.840
#> SRR1358143 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1379426 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1087007 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1086256 2 0.1163 0.95035 0.000 0.972 0.028
#> SRR1346734 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1082151 1 0.0829 0.92491 0.984 0.004 0.012
#> SRR1349320 2 0.0424 0.96277 0.000 0.992 0.008
#> SRR1317554 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1455878 1 0.5591 0.52292 0.696 0.000 0.304
#> SRR1446203 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1387397 1 0.6295 0.06801 0.528 0.000 0.472
#> SRR1402590 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1317532 1 0.6286 0.09520 0.536 0.000 0.464
#> SRR1331488 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1499675 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1440467 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR807995 2 0.3116 0.87606 0.000 0.892 0.108
#> SRR1476485 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1444083 1 0.5859 0.43544 0.656 0.000 0.344
#> SRR1313807 3 0.0424 0.89986 0.000 0.008 0.992
#> SRR1470751 1 0.0237 0.93407 0.996 0.004 0.000
#> SRR1403434 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1325290 3 0.6520 0.05224 0.488 0.004 0.508
#> SRR1070689 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1365313 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1321877 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR815711 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1433476 3 0.4235 0.74267 0.000 0.176 0.824
#> SRR1101883 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1433729 2 0.2878 0.88841 0.000 0.904 0.096
#> SRR1341877 3 0.5158 0.66079 0.232 0.004 0.764
#> SRR1090556 3 0.6505 0.12128 0.468 0.004 0.528
#> SRR1357389 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1404227 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1376830 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1478637 3 0.0237 0.90180 0.000 0.004 0.996
#> SRR1443776 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.90281 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.93740 1.000 0.000 0.000
#> SRR1349456 3 0.3879 0.76026 0.000 0.152 0.848
#> SRR1389384 1 0.6495 0.09673 0.536 0.004 0.460
#> SRR1316096 2 0.0000 0.96716 0.000 1.000 0.000
#> SRR1408512 1 0.3500 0.81775 0.880 0.004 0.116
#> SRR1447547 3 0.1765 0.87509 0.040 0.004 0.956
#> SRR1354053 2 0.0000 0.96716 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1353376 2 0.4636 0.7517 0.000 0.772 0.188 0.040
#> SRR1499040 3 0.6954 0.2134 0.384 0.000 0.500 0.116
#> SRR1322312 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.5691 0.5013 0.024 0.000 0.508 0.468
#> SRR1100991 3 0.4999 0.4899 0.000 0.000 0.508 0.492
#> SRR1349479 3 0.2081 0.6490 0.000 0.084 0.916 0.000
#> SRR1431248 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1405054 4 0.5682 0.1977 0.456 0.000 0.024 0.520
#> SRR1312266 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1409790 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1352507 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1383763 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1468314 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1473674 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR821043 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1455653 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.4072 0.6544 0.000 0.748 0.252 0.000
#> SRR1095383 2 0.0336 0.9378 0.000 0.992 0.008 0.000
#> SRR1479489 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1073435 4 0.0817 0.6960 0.000 0.000 0.024 0.976
#> SRR659649 3 0.4382 0.6102 0.000 0.000 0.704 0.296
#> SRR1395999 1 0.3266 0.7673 0.832 0.000 0.000 0.168
#> SRR1105248 4 0.5386 0.5020 0.000 0.056 0.236 0.708
#> SRR1338257 1 0.1637 0.8591 0.940 0.000 0.000 0.060
#> SRR1499395 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1489757 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1414637 4 0.0376 0.7160 0.004 0.004 0.000 0.992
#> SRR1478113 2 0.2011 0.8795 0.000 0.920 0.000 0.080
#> SRR1322477 4 0.4985 0.0014 0.468 0.000 0.000 0.532
#> SRR1478789 3 0.0188 0.7028 0.000 0.000 0.996 0.004
#> SRR1414185 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1376852 4 0.5000 -0.0850 0.496 0.000 0.000 0.504
#> SRR1323491 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1338103 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1472012 4 0.1022 0.7116 0.032 0.000 0.000 0.968
#> SRR1340325 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1334866 3 0.3074 0.5680 0.000 0.000 0.848 0.152
#> SRR1089446 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1344445 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1412969 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1071668 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1075804 1 0.5000 0.0550 0.504 0.000 0.000 0.496
#> SRR1383283 4 0.4713 0.3575 0.000 0.000 0.360 0.640
#> SRR1350239 4 0.2973 0.5200 0.000 0.000 0.144 0.856
#> SRR1353878 1 0.3074 0.7831 0.848 0.000 0.000 0.152
#> SRR1375721 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.3801 0.7113 0.780 0.000 0.000 0.220
#> SRR1090095 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1075102 2 0.3649 0.7441 0.000 0.796 0.000 0.204
#> SRR1098737 4 0.4985 0.0014 0.468 0.000 0.000 0.532
#> SRR1349409 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.3123 0.4960 0.000 0.000 0.156 0.844
#> SRR1407179 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1095913 3 0.3400 0.6518 0.000 0.000 0.820 0.180
#> SRR1403544 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR807971 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1436228 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1445218 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1485438 4 0.5209 0.5822 0.000 0.140 0.104 0.756
#> SRR1358143 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.3801 0.7113 0.780 0.000 0.000 0.220
#> SRR1380806 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1087007 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1086256 4 0.5833 0.0674 0.000 0.440 0.032 0.528
#> SRR1346734 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1082151 1 0.4661 0.6472 0.728 0.000 0.016 0.256
#> SRR1349320 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1317554 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1076022 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1339573 3 0.0336 0.7039 0.000 0.000 0.992 0.008
#> SRR1455878 4 0.3311 0.6421 0.172 0.000 0.000 0.828
#> SRR1446203 3 0.4866 0.5604 0.000 0.000 0.596 0.404
#> SRR1387397 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1402590 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1317532 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1331488 1 0.4989 0.1275 0.528 0.000 0.000 0.472
#> SRR1499675 4 0.3123 0.6238 0.000 0.000 0.156 0.844
#> SRR1440467 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR807995 2 0.4454 0.5221 0.000 0.692 0.000 0.308
#> SRR1476485 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.3356 0.7598 0.824 0.000 0.000 0.176
#> SRR1456051 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1444083 4 0.4948 0.1782 0.440 0.000 0.000 0.560
#> SRR1313807 3 0.4605 0.2767 0.000 0.000 0.664 0.336
#> SRR1470751 1 0.4360 0.6666 0.744 0.000 0.008 0.248
#> SRR1403434 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1325290 4 0.0592 0.7156 0.016 0.000 0.000 0.984
#> SRR1070689 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1365313 3 0.4564 0.2629 0.000 0.000 0.672 0.328
#> SRR1321877 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR815711 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1433476 3 0.3355 0.5771 0.000 0.160 0.836 0.004
#> SRR1101883 3 0.4989 0.5212 0.000 0.000 0.528 0.472
#> SRR1433729 2 0.3790 0.7927 0.000 0.820 0.164 0.016
#> SRR1341877 4 0.1716 0.6927 0.000 0.000 0.064 0.936
#> SRR1090556 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1357389 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1404227 3 0.1211 0.6977 0.000 0.000 0.960 0.040
#> SRR1376830 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.4477 0.4573 0.688 0.000 0.000 0.312
#> SRR1080294 2 0.1792 0.8952 0.000 0.932 0.068 0.000
#> SRR1336314 2 0.0336 0.9370 0.000 0.992 0.000 0.008
#> SRR1102152 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1478637 4 0.2704 0.6541 0.000 0.000 0.124 0.876
#> SRR1443776 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.4985 0.5275 0.000 0.000 0.532 0.468
#> SRR1080117 3 0.0000 0.7045 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1091865 1 0.3801 0.7113 0.780 0.000 0.000 0.220
#> SRR1361072 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.0188 0.7028 0.000 0.000 0.996 0.004
#> SRR1389384 1 0.7641 0.1142 0.440 0.000 0.344 0.216
#> SRR1316096 2 0.0000 0.9422 0.000 1.000 0.000 0.000
#> SRR1408512 4 0.4761 0.2978 0.372 0.000 0.000 0.628
#> SRR1447547 4 0.0000 0.7153 0.000 0.000 0.000 1.000
#> SRR1354053 2 0.0000 0.9422 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 2 0.3521 0.6907 0.000 0.764 0.232 0.004 0.000
#> SRR1499040 1 0.4307 -0.0598 0.504 0.000 0.000 0.000 0.496
#> SRR1322312 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1100991 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1349479 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1431248 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1405054 4 0.0162 0.9299 0.000 0.000 0.000 0.996 0.004
#> SRR1312266 1 0.2690 0.7827 0.844 0.000 0.000 0.000 0.156
#> SRR1409790 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1352507 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1383763 1 0.0162 0.9429 0.996 0.000 0.000 0.000 0.004
#> SRR1468314 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1473674 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1390499 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR821043 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1455653 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1335236 2 0.4182 0.3210 0.000 0.600 0.400 0.000 0.000
#> SRR1095383 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1479489 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1073435 5 0.6478 0.0134 0.000 0.000 0.184 0.396 0.420
#> SRR659649 4 0.3003 0.7371 0.000 0.000 0.188 0.812 0.000
#> SRR1395999 5 0.4268 0.3649 0.444 0.000 0.000 0.000 0.556
#> SRR1105248 4 0.3242 0.6961 0.000 0.000 0.216 0.784 0.000
#> SRR1338257 1 0.3752 0.5622 0.708 0.000 0.000 0.000 0.292
#> SRR1499395 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 2 0.1121 0.8945 0.000 0.956 0.000 0.000 0.044
#> SRR1489757 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1414637 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1478113 2 0.3491 0.6728 0.000 0.768 0.000 0.004 0.228
#> SRR1322477 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1478789 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1414185 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1376852 5 0.2773 0.7328 0.164 0.000 0.000 0.000 0.836
#> SRR1323491 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1338103 5 0.3395 0.6322 0.000 0.000 0.000 0.236 0.764
#> SRR1472012 5 0.1608 0.7560 0.000 0.000 0.000 0.072 0.928
#> SRR1340325 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.3816 0.5032 0.000 0.000 0.304 0.000 0.696
#> SRR1089446 4 0.3123 0.7677 0.000 0.000 0.160 0.828 0.012
#> SRR1344445 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1412969 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1071668 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1075804 5 0.2690 0.7357 0.156 0.000 0.000 0.000 0.844
#> SRR1383283 3 0.2605 0.7915 0.000 0.000 0.852 0.148 0.000
#> SRR1350239 4 0.0000 0.9306 0.000 0.000 0.000 1.000 0.000
#> SRR1353878 5 0.4171 0.3641 0.396 0.000 0.000 0.000 0.604
#> SRR1375721 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1083983 5 0.3730 0.5768 0.288 0.000 0.000 0.000 0.712
#> SRR1090095 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 2 0.3612 0.6700 0.000 0.764 0.000 0.008 0.228
#> SRR1098737 5 0.2690 0.7357 0.156 0.000 0.000 0.000 0.844
#> SRR1349409 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.0000 0.9306 0.000 0.000 0.000 1.000 0.000
#> SRR1407179 5 0.3932 0.4957 0.000 0.000 0.000 0.328 0.672
#> SRR1095913 3 0.3109 0.7267 0.000 0.000 0.800 0.200 0.000
#> SRR1403544 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR807971 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1436228 5 0.0510 0.7690 0.000 0.000 0.000 0.016 0.984
#> SRR1445218 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1485438 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1358143 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 5 0.4602 0.6136 0.240 0.000 0.000 0.052 0.708
#> SRR1380806 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 5 0.2674 0.7073 0.000 0.140 0.000 0.004 0.856
#> SRR1346734 2 0.0162 0.9195 0.000 0.996 0.000 0.004 0.000
#> SRR1414515 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.3561 0.6155 0.260 0.000 0.000 0.000 0.740
#> SRR1349320 2 0.0451 0.9163 0.000 0.988 0.000 0.004 0.008
#> SRR1317554 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1076022 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1339573 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1455878 5 0.2248 0.7501 0.012 0.000 0.000 0.088 0.900
#> SRR1446203 3 0.4101 0.4132 0.000 0.000 0.628 0.372 0.000
#> SRR1387397 5 0.0703 0.7683 0.000 0.000 0.000 0.024 0.976
#> SRR1402590 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.2690 0.7094 0.000 0.000 0.000 0.156 0.844
#> SRR1331488 5 0.3074 0.7165 0.196 0.000 0.000 0.000 0.804
#> SRR1499675 5 0.5970 0.4223 0.000 0.000 0.184 0.228 0.588
#> SRR1440467 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR807995 2 0.3730 0.5871 0.000 0.712 0.000 0.000 0.288
#> SRR1476485 2 0.0162 0.9195 0.000 0.996 0.000 0.004 0.000
#> SRR1388214 5 0.3796 0.5607 0.300 0.000 0.000 0.000 0.700
#> SRR1456051 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 4 0.1197 0.9008 0.000 0.000 0.048 0.952 0.000
#> SRR1444083 4 0.4390 0.2002 0.004 0.000 0.000 0.568 0.428
#> SRR1313807 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1470751 5 0.3612 0.6056 0.268 0.000 0.000 0.000 0.732
#> SRR1403434 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1390540 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1325290 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1070689 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.3730 0.5547 0.000 0.000 0.712 0.000 0.288
#> SRR1321877 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR815711 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1433476 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1101883 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1433729 2 0.3300 0.7280 0.000 0.792 0.204 0.004 0.000
#> SRR1341877 5 0.2690 0.7094 0.000 0.000 0.000 0.156 0.844
#> SRR1090556 5 0.2690 0.7094 0.000 0.000 0.000 0.156 0.844
#> SRR1357389 4 0.0162 0.9334 0.000 0.000 0.004 0.996 0.000
#> SRR1404227 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1376830 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.3730 0.5032 0.712 0.000 0.000 0.000 0.288
#> SRR1080294 2 0.1410 0.8826 0.000 0.940 0.060 0.000 0.000
#> SRR1336314 2 0.1205 0.8951 0.000 0.956 0.000 0.004 0.040
#> SRR1102152 1 0.2690 0.7827 0.844 0.000 0.000 0.000 0.156
#> SRR1345244 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 5 0.0162 0.7701 0.000 0.000 0.000 0.004 0.996
#> SRR1443776 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 3 0.4161 0.3666 0.000 0.000 0.608 0.392 0.000
#> SRR1080117 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1091865 5 0.3730 0.5768 0.288 0.000 0.000 0.000 0.712
#> SRR1361072 1 0.1270 0.8936 0.948 0.000 0.000 0.000 0.052
#> SRR1487890 1 0.0000 0.9464 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.0000 0.9307 0.000 0.000 1.000 0.000 0.000
#> SRR1389384 5 0.1792 0.7555 0.084 0.000 0.000 0.000 0.916
#> SRR1316096 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> SRR1408512 5 0.0000 0.7703 0.000 0.000 0.000 0.000 1.000
#> SRR1447547 5 0.4171 0.3615 0.000 0.000 0.000 0.396 0.604
#> SRR1354053 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.0291 0.7742 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1499040 1 0.4086 0.0299 0.528 0.000 0.000 0.008 0.464 0.000
#> SRR1322312 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1100991 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1349479 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1431248 5 0.0632 0.7757 0.000 0.000 0.000 0.024 0.976 0.000
#> SRR1405054 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1312266 1 0.2212 0.8342 0.880 0.000 0.000 0.008 0.112 0.000
#> SRR1409790 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1352507 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1383763 1 0.0146 0.9490 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1468314 4 0.3862 0.4177 0.000 0.476 0.000 0.524 0.000 0.000
#> SRR1473674 2 0.0146 0.9190 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1390499 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.2823 0.7133 0.000 0.204 0.000 0.796 0.000 0.000
#> SRR1455653 4 0.2003 0.7506 0.000 0.116 0.000 0.884 0.000 0.000
#> SRR1335236 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1095383 4 0.3862 0.4177 0.000 0.476 0.000 0.524 0.000 0.000
#> SRR1479489 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1073435 5 0.5816 0.0185 0.000 0.000 0.388 0.000 0.428 0.184
#> SRR659649 3 0.2664 0.7376 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1395999 5 0.3684 0.5193 0.372 0.000 0.000 0.000 0.628 0.000
#> SRR1105248 3 0.3023 0.6780 0.000 0.000 0.768 0.000 0.000 0.232
#> SRR1338257 1 0.3323 0.6486 0.752 0.000 0.000 0.008 0.240 0.000
#> SRR1499395 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1350002 2 0.0146 0.9180 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1489757 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1414637 5 0.0260 0.7761 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1478113 4 0.0146 0.7739 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1322477 5 0.0260 0.7761 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1478789 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1414185 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 5 0.2260 0.7465 0.140 0.000 0.000 0.000 0.860 0.000
#> SRR1323491 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338103 5 0.2941 0.6518 0.000 0.000 0.220 0.000 0.780 0.000
#> SRR1472012 5 0.1141 0.7748 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1340325 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1087321 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1334866 5 0.3595 0.5556 0.000 0.000 0.000 0.008 0.704 0.288
#> SRR1089446 3 0.3149 0.7790 0.000 0.000 0.824 0.000 0.044 0.132
#> SRR1344445 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1412969 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1075804 5 0.1957 0.7574 0.112 0.000 0.000 0.000 0.888 0.000
#> SRR1383283 6 0.2340 0.7943 0.000 0.000 0.148 0.000 0.000 0.852
#> SRR1350239 3 0.1714 0.8665 0.000 0.000 0.908 0.092 0.000 0.000
#> SRR1353878 5 0.4051 0.2848 0.432 0.000 0.000 0.008 0.560 0.000
#> SRR1375721 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 5 0.3349 0.6562 0.244 0.000 0.000 0.008 0.748 0.000
#> SRR1090095 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0260 0.7713 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1098737 5 0.1957 0.7574 0.112 0.000 0.000 0.000 0.888 0.000
#> SRR1349409 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 3 0.0865 0.9063 0.000 0.000 0.964 0.036 0.000 0.000
#> SRR1407179 5 0.3446 0.5194 0.000 0.000 0.308 0.000 0.692 0.000
#> SRR1095913 6 0.2793 0.7293 0.000 0.000 0.200 0.000 0.000 0.800
#> SRR1403544 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1436228 5 0.0458 0.7785 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1445218 2 0.0146 0.9189 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1485438 2 0.3998 -0.0460 0.000 0.504 0.000 0.004 0.492 0.000
#> SRR1358143 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1328760 5 0.4105 0.6550 0.236 0.000 0.036 0.008 0.720 0.000
#> SRR1380806 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1379426 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1087007 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1086256 5 0.2333 0.7303 0.000 0.004 0.000 0.120 0.872 0.004
#> SRR1346734 4 0.0260 0.7761 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1414515 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 5 0.4254 0.6647 0.216 0.000 0.000 0.072 0.712 0.000
#> SRR1349320 4 0.0260 0.7761 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1317554 4 0.2823 0.7133 0.000 0.204 0.000 0.796 0.000 0.000
#> SRR1076022 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339573 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1455878 5 0.1606 0.7756 0.008 0.000 0.056 0.004 0.932 0.000
#> SRR1446203 6 0.3672 0.4203 0.000 0.000 0.368 0.000 0.000 0.632
#> SRR1387397 5 0.0520 0.7779 0.000 0.000 0.008 0.008 0.984 0.000
#> SRR1402590 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.1957 0.7509 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1331488 5 0.2664 0.7280 0.184 0.000 0.000 0.000 0.816 0.000
#> SRR1499675 5 0.4937 0.5234 0.000 0.000 0.196 0.000 0.652 0.152
#> SRR1440467 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476485 4 0.0260 0.7761 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1388214 5 0.3575 0.6076 0.284 0.000 0.000 0.008 0.708 0.000
#> SRR1456051 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1473275 3 0.1007 0.8960 0.000 0.000 0.956 0.000 0.000 0.044
#> SRR1444083 3 0.4456 0.3447 0.024 0.000 0.608 0.008 0.360 0.000
#> SRR1313807 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1470751 5 0.3245 0.6750 0.228 0.000 0.000 0.008 0.764 0.000
#> SRR1403434 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1325290 5 0.0260 0.7761 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1070689 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.3151 0.6247 0.000 0.000 0.000 0.000 0.252 0.748
#> SRR1321877 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1433476 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1101883 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1433729 4 0.4853 0.2922 0.000 0.004 0.052 0.556 0.000 0.388
#> SRR1341877 5 0.1957 0.7509 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1090556 5 0.1957 0.7509 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1357389 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1404227 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.3126 0.6026 0.752 0.000 0.000 0.000 0.248 0.000
#> SRR1080294 4 0.3991 0.4177 0.000 0.472 0.000 0.524 0.000 0.004
#> SRR1336314 4 0.0260 0.7761 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1102152 1 0.2212 0.8342 0.880 0.000 0.000 0.008 0.112 0.000
#> SRR1345244 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1478637 5 0.0146 0.7767 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1443776 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 6 0.3727 0.3738 0.000 0.000 0.388 0.000 0.000 0.612
#> SRR1080117 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.9218 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091865 5 0.3349 0.6562 0.244 0.000 0.000 0.008 0.748 0.000
#> SRR1361072 1 0.1663 0.8584 0.912 0.000 0.000 0.000 0.088 0.000
#> SRR1487890 1 0.0000 0.9524 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1349456 6 0.0000 0.9291 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1389384 5 0.1701 0.7655 0.072 0.000 0.000 0.008 0.920 0.000
#> SRR1316096 2 0.0458 0.9048 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1408512 5 0.0000 0.7761 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1447547 5 0.5479 0.1663 0.000 0.000 0.388 0.128 0.484 0.000
#> SRR1354053 4 0.3862 0.4177 0.000 0.476 0.000 0.524 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.402 0.904 0.905 0.3340 0.688 0.688
#> 3 3 0.893 0.921 0.967 0.9643 0.532 0.376
#> 4 4 0.904 0.840 0.930 0.0131 0.811 0.571
#> 5 5 0.797 0.787 0.873 0.1099 0.894 0.700
#> 6 6 0.735 0.737 0.812 0.0787 0.889 0.603
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.912 1.000 0.000
#> SRR1349562 1 0.0000 0.912 1.000 0.000
#> SRR1353376 2 0.6623 0.957 0.172 0.828
#> SRR1499040 1 0.2603 0.911 0.956 0.044
#> SRR1322312 1 0.0000 0.912 1.000 0.000
#> SRR1324412 1 0.7602 0.849 0.780 0.220
#> SRR1100991 1 0.7602 0.849 0.780 0.220
#> SRR1349479 1 0.5059 0.895 0.888 0.112
#> SRR1431248 1 0.2778 0.911 0.952 0.048
#> SRR1405054 1 0.7528 0.849 0.784 0.216
#> SRR1312266 1 0.1843 0.913 0.972 0.028
#> SRR1409790 1 0.7602 0.849 0.780 0.220
#> SRR1352507 1 0.7602 0.849 0.780 0.220
#> SRR1383763 1 0.0938 0.913 0.988 0.012
#> SRR1468314 2 0.4939 0.958 0.108 0.892
#> SRR1473674 2 0.6623 0.957 0.172 0.828
#> SRR1390499 1 0.0000 0.912 1.000 0.000
#> SRR821043 2 0.6438 0.958 0.164 0.836
#> SRR1455653 2 0.6531 0.958 0.168 0.832
#> SRR1335236 2 0.4939 0.958 0.108 0.892
#> SRR1095383 2 0.4939 0.958 0.108 0.892
#> SRR1479489 1 0.1414 0.913 0.980 0.020
#> SRR1310433 2 0.4939 0.958 0.108 0.892
#> SRR1073435 1 0.5059 0.895 0.888 0.112
#> SRR659649 1 0.7602 0.849 0.780 0.220
#> SRR1395999 1 0.0000 0.912 1.000 0.000
#> SRR1105248 1 0.5059 0.895 0.888 0.112
#> SRR1338257 1 0.0000 0.912 1.000 0.000
#> SRR1499395 1 0.7602 0.849 0.780 0.220
#> SRR1350002 2 0.6623 0.957 0.172 0.828
#> SRR1489757 1 0.7602 0.849 0.780 0.220
#> SRR1414637 1 0.2778 0.911 0.952 0.048
#> SRR1478113 2 0.6623 0.957 0.172 0.828
#> SRR1322477 1 0.2778 0.911 0.952 0.048
#> SRR1478789 1 0.5059 0.895 0.888 0.112
#> SRR1414185 1 0.7602 0.849 0.780 0.220
#> SRR1069141 2 0.4939 0.958 0.108 0.892
#> SRR1376852 1 0.0000 0.912 1.000 0.000
#> SRR1323491 1 0.0000 0.912 1.000 0.000
#> SRR1338103 1 0.2778 0.911 0.952 0.048
#> SRR1472012 1 0.2603 0.911 0.956 0.044
#> SRR1340325 1 0.1633 0.913 0.976 0.024
#> SRR1087321 1 0.7602 0.849 0.780 0.220
#> SRR1488790 1 0.0000 0.912 1.000 0.000
#> SRR1334866 1 0.2948 0.910 0.948 0.052
#> SRR1089446 1 0.7602 0.849 0.780 0.220
#> SRR1344445 1 0.7602 0.849 0.780 0.220
#> SRR1412969 1 0.5059 0.895 0.888 0.112
#> SRR1071668 1 0.7602 0.849 0.780 0.220
#> SRR1075804 1 0.0000 0.912 1.000 0.000
#> SRR1383283 1 0.5059 0.895 0.888 0.112
#> SRR1350239 1 0.5059 0.895 0.888 0.112
#> SRR1353878 1 0.0000 0.912 1.000 0.000
#> SRR1375721 1 0.0000 0.912 1.000 0.000
#> SRR1083983 1 0.0000 0.912 1.000 0.000
#> SRR1090095 1 0.0000 0.912 1.000 0.000
#> SRR1414792 1 0.0000 0.912 1.000 0.000
#> SRR1075102 2 0.6623 0.957 0.172 0.828
#> SRR1098737 1 0.0000 0.912 1.000 0.000
#> SRR1349409 1 0.0000 0.912 1.000 0.000
#> SRR1413008 1 0.5059 0.895 0.888 0.112
#> SRR1407179 1 0.5059 0.895 0.888 0.112
#> SRR1095913 1 0.5059 0.895 0.888 0.112
#> SRR1403544 1 0.0000 0.912 1.000 0.000
#> SRR1490546 1 0.0000 0.912 1.000 0.000
#> SRR807971 1 0.7602 0.849 0.780 0.220
#> SRR1436228 1 0.2778 0.911 0.952 0.048
#> SRR1445218 2 0.4939 0.958 0.108 0.892
#> SRR1485438 2 0.6623 0.957 0.172 0.828
#> SRR1358143 1 0.0000 0.912 1.000 0.000
#> SRR1328760 1 0.0000 0.912 1.000 0.000
#> SRR1380806 1 0.0376 0.912 0.996 0.004
#> SRR1379426 1 0.7602 0.849 0.780 0.220
#> SRR1087007 1 0.7602 0.849 0.780 0.220
#> SRR1086256 1 0.2778 0.911 0.952 0.048
#> SRR1346734 2 0.6623 0.957 0.172 0.828
#> SRR1414515 1 0.0000 0.912 1.000 0.000
#> SRR1082151 1 0.2778 0.911 0.952 0.048
#> SRR1349320 2 0.6623 0.957 0.172 0.828
#> SRR1317554 2 0.4939 0.958 0.108 0.892
#> SRR1076022 2 0.5178 0.959 0.116 0.884
#> SRR1339573 1 0.7602 0.849 0.780 0.220
#> SRR1455878 1 0.0000 0.912 1.000 0.000
#> SRR1446203 1 0.7602 0.849 0.780 0.220
#> SRR1387397 1 0.1843 0.913 0.972 0.028
#> SRR1402590 1 0.0000 0.912 1.000 0.000
#> SRR1317532 1 0.1414 0.913 0.980 0.020
#> SRR1331488 1 0.0000 0.912 1.000 0.000
#> SRR1499675 1 0.2778 0.911 0.952 0.048
#> SRR1440467 1 0.7602 0.849 0.780 0.220
#> SRR807995 2 0.6623 0.957 0.172 0.828
#> SRR1476485 2 0.6623 0.957 0.172 0.828
#> SRR1388214 1 0.0000 0.912 1.000 0.000
#> SRR1456051 1 0.0000 0.912 1.000 0.000
#> SRR1473275 1 0.7602 0.849 0.780 0.220
#> SRR1444083 1 0.0000 0.912 1.000 0.000
#> SRR1313807 1 0.5059 0.895 0.888 0.112
#> SRR1470751 1 0.2778 0.911 0.952 0.048
#> SRR1403434 1 0.7602 0.849 0.780 0.220
#> SRR1390540 1 0.0000 0.912 1.000 0.000
#> SRR1093861 2 0.4939 0.958 0.108 0.892
#> SRR1325290 1 0.2778 0.911 0.952 0.048
#> SRR1070689 1 0.0000 0.912 1.000 0.000
#> SRR1384049 1 0.0000 0.912 1.000 0.000
#> SRR1081184 1 0.0000 0.912 1.000 0.000
#> SRR1324295 1 0.0000 0.912 1.000 0.000
#> SRR1365313 1 0.5059 0.895 0.888 0.112
#> SRR1321877 1 0.7602 0.849 0.780 0.220
#> SRR815711 1 0.7602 0.849 0.780 0.220
#> SRR1433476 1 0.5059 0.895 0.888 0.112
#> SRR1101883 1 0.7602 0.849 0.780 0.220
#> SRR1433729 1 0.8207 0.732 0.744 0.256
#> SRR1341877 1 0.2778 0.911 0.952 0.048
#> SRR1090556 1 0.2778 0.911 0.952 0.048
#> SRR1357389 1 0.7602 0.849 0.780 0.220
#> SRR1404227 1 0.5059 0.895 0.888 0.112
#> SRR1376830 1 0.0000 0.912 1.000 0.000
#> SRR1500661 1 0.0000 0.912 1.000 0.000
#> SRR1080294 2 0.4939 0.958 0.108 0.892
#> SRR1336314 2 0.6623 0.957 0.172 0.828
#> SRR1102152 1 0.0000 0.912 1.000 0.000
#> SRR1345244 1 0.7602 0.849 0.780 0.220
#> SRR1478637 1 0.2778 0.911 0.952 0.048
#> SRR1443776 1 0.7602 0.849 0.780 0.220
#> SRR1120939 1 0.7602 0.849 0.780 0.220
#> SRR1080117 1 0.7602 0.849 0.780 0.220
#> SRR1102899 2 0.4939 0.958 0.108 0.892
#> SRR1091865 1 0.0376 0.912 0.996 0.004
#> SRR1361072 1 0.0000 0.912 1.000 0.000
#> SRR1487890 1 0.0000 0.912 1.000 0.000
#> SRR1349456 1 0.5059 0.895 0.888 0.112
#> SRR1389384 1 0.2778 0.911 0.952 0.048
#> SRR1316096 2 0.4939 0.958 0.108 0.892
#> SRR1408512 1 0.0672 0.912 0.992 0.008
#> SRR1447547 1 0.2778 0.911 0.952 0.048
#> SRR1354053 2 0.4939 0.958 0.108 0.892
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1499040 1 0.9758 -0.0345 0.412 0.232 0.356
#> SRR1322312 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1100991 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1349479 2 0.0747 0.9467 0.000 0.984 0.016
#> SRR1431248 2 0.5926 0.4410 0.356 0.644 0.000
#> SRR1405054 3 0.3192 0.8572 0.112 0.000 0.888
#> SRR1312266 1 0.5138 0.6610 0.748 0.252 0.000
#> SRR1409790 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1383763 1 0.3752 0.8257 0.856 0.144 0.000
#> SRR1468314 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1335236 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1095383 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1073435 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR659649 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1105248 2 0.0592 0.9497 0.000 0.988 0.012
#> SRR1338257 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1414637 2 0.3816 0.8060 0.148 0.852 0.000
#> SRR1478113 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1322477 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1478789 2 0.5810 0.5036 0.000 0.664 0.336
#> SRR1414185 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1338103 1 0.4555 0.7484 0.800 0.200 0.000
#> SRR1472012 1 0.4235 0.7811 0.824 0.176 0.000
#> SRR1340325 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1334866 2 0.4235 0.7734 0.176 0.824 0.000
#> SRR1089446 3 0.3879 0.8155 0.000 0.152 0.848
#> SRR1344445 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1412969 3 0.1860 0.9244 0.000 0.052 0.948
#> SRR1071668 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1383283 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1350239 2 0.0747 0.9467 0.000 0.984 0.016
#> SRR1353878 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1413008 2 0.0747 0.9467 0.000 0.984 0.016
#> SRR1407179 3 0.4796 0.7062 0.000 0.220 0.780
#> SRR1095913 2 0.5835 0.4953 0.000 0.660 0.340
#> SRR1403544 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1436228 2 0.1643 0.9178 0.044 0.956 0.000
#> SRR1445218 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1082151 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1349320 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1499675 1 0.5529 0.5833 0.704 0.296 0.000
#> SRR1440467 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1444083 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1313807 2 0.0237 0.9552 0.000 0.996 0.004
#> SRR1470751 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1403434 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1325290 1 0.0892 0.9471 0.980 0.020 0.000
#> SRR1070689 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1365313 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1321877 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1433476 2 0.0592 0.9497 0.000 0.988 0.012
#> SRR1101883 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1341877 1 0.4062 0.7970 0.836 0.164 0.000
#> SRR1090556 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1404227 3 0.5529 0.5641 0.000 0.296 0.704
#> SRR1376830 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1478637 2 0.0237 0.9547 0.004 0.996 0.000
#> SRR1443776 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.9710 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1349456 2 0.5785 0.5123 0.000 0.668 0.332
#> SRR1389384 1 0.1163 0.9406 0.972 0.028 0.000
#> SRR1316096 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.9639 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.9576 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.9576 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1349562 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1353376 4 0.2081 0.8232 0.000 0.084 0.000 0.916
#> SRR1499040 1 0.4817 0.3613 0.612 0.000 0.388 0.000
#> SRR1322312 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1324412 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1349479 3 0.1792 0.8672 0.000 0.000 0.932 0.068
#> SRR1431248 1 0.1888 0.9197 0.940 0.044 0.000 0.016
#> SRR1405054 3 0.3688 0.6342 0.208 0.000 0.792 0.000
#> SRR1312266 1 0.1109 0.9422 0.968 0.028 0.000 0.004
#> SRR1409790 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1468314 2 0.4916 0.2204 0.000 0.576 0.000 0.424
#> SRR1473674 2 0.1118 0.8644 0.000 0.964 0.000 0.036
#> SRR1390499 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR821043 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1455653 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1335236 2 0.1792 0.8294 0.000 0.932 0.000 0.068
#> SRR1095383 2 0.4985 0.2132 0.000 0.532 0.000 0.468
#> SRR1479489 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1310433 2 0.0817 0.8642 0.000 0.976 0.000 0.024
#> SRR1073435 4 0.7588 0.0563 0.116 0.020 0.408 0.456
#> SRR659649 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1105248 3 0.4985 0.1918 0.000 0.000 0.532 0.468
#> SRR1338257 1 0.0188 0.9599 0.996 0.004 0.000 0.000
#> SRR1499395 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.1302 0.8592 0.000 0.956 0.000 0.044
#> SRR1489757 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1414637 1 0.1888 0.9197 0.940 0.044 0.000 0.016
#> SRR1478113 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1322477 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1478789 3 0.1938 0.8715 0.000 0.012 0.936 0.052
#> SRR1414185 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0921 0.8633 0.000 0.972 0.000 0.028
#> SRR1376852 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.0188 0.9592 0.996 0.000 0.000 0.004
#> SRR1472012 1 0.0188 0.9592 0.996 0.000 0.000 0.004
#> SRR1340325 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0336 0.9004 0.000 0.000 0.992 0.008
#> SRR1488790 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1334866 1 0.2197 0.9108 0.928 0.024 0.000 0.048
#> SRR1089446 3 0.3444 0.6647 0.184 0.000 0.816 0.000
#> SRR1344445 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.1302 0.8821 0.000 0.000 0.956 0.044
#> SRR1071668 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1383283 3 0.6754 0.0265 0.000 0.092 0.464 0.444
#> SRR1350239 3 0.5155 0.1828 0.004 0.000 0.528 0.468
#> SRR1353878 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1083983 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1090095 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1414792 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1075102 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1098737 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1413008 3 0.5755 0.1986 0.028 0.000 0.528 0.444
#> SRR1407179 3 0.1474 0.8778 0.000 0.000 0.948 0.052
#> SRR1095913 3 0.4144 0.7716 0.000 0.104 0.828 0.068
#> SRR1403544 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1490546 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1436228 1 0.0927 0.9494 0.976 0.008 0.000 0.016
#> SRR1445218 2 0.1118 0.8644 0.000 0.964 0.000 0.036
#> SRR1485438 2 0.1297 0.8419 0.020 0.964 0.000 0.016
#> SRR1358143 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1328760 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0188 0.9603 0.996 0.004 0.000 0.000
#> SRR1379426 3 0.0188 0.9021 0.000 0.000 0.996 0.004
#> SRR1087007 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1086256 1 0.5695 0.0533 0.500 0.476 0.000 0.024
#> SRR1346734 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1414515 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1082151 1 0.2002 0.9167 0.936 0.044 0.000 0.020
#> SRR1349320 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1317554 4 0.3266 0.7217 0.000 0.168 0.000 0.832
#> SRR1076022 2 0.1118 0.8644 0.000 0.964 0.000 0.036
#> SRR1339573 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1317532 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1331488 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1499675 1 0.1389 0.9300 0.952 0.000 0.000 0.048
#> SRR1440467 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR807995 2 0.1118 0.8644 0.000 0.964 0.000 0.036
#> SRR1476485 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1388214 1 0.0188 0.9599 0.996 0.004 0.000 0.000
#> SRR1456051 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1473275 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.0188 0.9599 0.996 0.004 0.000 0.000
#> SRR1313807 3 0.2048 0.8651 0.000 0.008 0.928 0.064
#> SRR1470751 1 0.2214 0.9102 0.928 0.044 0.000 0.028
#> SRR1403434 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0921 0.8633 0.000 0.972 0.000 0.028
#> SRR1325290 1 0.0188 0.9592 0.996 0.000 0.000 0.004
#> SRR1070689 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1384049 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1081184 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1324295 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1365313 1 0.6603 0.5966 0.696 0.080 0.168 0.056
#> SRR1321877 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR815711 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1433476 3 0.5151 0.1930 0.000 0.004 0.532 0.464
#> SRR1101883 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1433729 4 0.7307 0.0564 0.000 0.376 0.156 0.468
#> SRR1341877 1 0.0188 0.9592 0.996 0.000 0.000 0.004
#> SRR1090556 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1357389 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.1389 0.8799 0.000 0.000 0.952 0.048
#> SRR1376830 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1500661 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1080294 2 0.5155 0.2048 0.000 0.528 0.004 0.468
#> SRR1336314 4 0.1302 0.8532 0.000 0.044 0.000 0.956
#> SRR1102152 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1478637 1 0.3636 0.7842 0.820 0.172 0.000 0.008
#> SRR1443776 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.9037 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.1557 0.8421 0.000 0.944 0.000 0.056
#> SRR1091865 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1361072 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0707 0.9577 0.980 0.020 0.000 0.000
#> SRR1349456 3 0.3687 0.8018 0.000 0.080 0.856 0.064
#> SRR1389384 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1316096 2 0.0921 0.8649 0.000 0.972 0.000 0.028
#> SRR1408512 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> SRR1447547 1 0.3945 0.7273 0.780 0.000 0.004 0.216
#> SRR1354053 4 0.2011 0.8292 0.000 0.080 0.000 0.920
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0451 0.957580 0.988 0.000 0.000 0.004 0.008
#> SRR1349562 1 0.1831 0.904097 0.920 0.000 0.000 0.004 0.076
#> SRR1353376 4 0.1830 0.878031 0.000 0.040 0.000 0.932 0.028
#> SRR1499040 5 0.6990 0.491015 0.280 0.008 0.312 0.000 0.400
#> SRR1322312 1 0.0771 0.954275 0.976 0.000 0.000 0.004 0.020
#> SRR1324412 3 0.1153 0.860931 0.004 0.000 0.964 0.024 0.008
#> SRR1100991 3 0.1153 0.860931 0.004 0.000 0.964 0.024 0.008
#> SRR1349479 3 0.6777 0.173139 0.000 0.024 0.472 0.360 0.144
#> SRR1431248 5 0.4295 0.849745 0.236 0.028 0.000 0.004 0.732
#> SRR1405054 3 0.4943 0.315768 0.376 0.000 0.596 0.016 0.012
#> SRR1312266 1 0.0740 0.953349 0.980 0.008 0.000 0.004 0.008
#> SRR1409790 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1352507 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1383763 1 0.1648 0.908616 0.940 0.020 0.000 0.000 0.040
#> SRR1468314 2 0.6218 0.080442 0.000 0.488 0.000 0.364 0.148
#> SRR1473674 2 0.0324 0.771487 0.000 0.992 0.000 0.004 0.004
#> SRR1390499 1 0.0671 0.955482 0.980 0.000 0.000 0.004 0.016
#> SRR821043 4 0.1544 0.880801 0.000 0.068 0.000 0.932 0.000
#> SRR1455653 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1335236 2 0.0771 0.770176 0.000 0.976 0.000 0.004 0.020
#> SRR1095383 2 0.6294 0.050897 0.000 0.468 0.000 0.376 0.156
#> SRR1479489 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1310433 2 0.0451 0.773658 0.000 0.988 0.000 0.004 0.008
#> SRR1073435 3 0.7462 0.229097 0.004 0.040 0.448 0.216 0.292
#> SRR659649 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1395999 1 0.0404 0.957168 0.988 0.000 0.000 0.000 0.012
#> SRR1105248 3 0.6888 0.128241 0.000 0.020 0.448 0.360 0.172
#> SRR1338257 1 0.0798 0.952904 0.976 0.000 0.000 0.008 0.016
#> SRR1499395 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1350002 2 0.1282 0.742778 0.000 0.952 0.000 0.044 0.004
#> SRR1489757 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1414637 5 0.4240 0.847379 0.228 0.036 0.000 0.000 0.736
#> SRR1478113 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1322477 5 0.3612 0.843018 0.268 0.000 0.000 0.000 0.732
#> SRR1478789 3 0.2124 0.832687 0.000 0.056 0.916 0.000 0.028
#> SRR1414185 3 0.0000 0.866334 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 2 0.0566 0.773067 0.000 0.984 0.000 0.004 0.012
#> SRR1376852 1 0.0510 0.956193 0.984 0.000 0.000 0.000 0.016
#> SRR1323491 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1338103 5 0.4437 0.805478 0.316 0.020 0.000 0.000 0.664
#> SRR1472012 5 0.4829 0.468649 0.484 0.020 0.000 0.000 0.496
#> SRR1340325 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1087321 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1488790 1 0.0566 0.956717 0.984 0.000 0.000 0.004 0.012
#> SRR1334866 5 0.4185 0.840091 0.216 0.024 0.008 0.000 0.752
#> SRR1089446 3 0.1205 0.841929 0.040 0.000 0.956 0.004 0.000
#> SRR1344445 3 0.0290 0.865836 0.000 0.000 0.992 0.000 0.008
#> SRR1412969 3 0.0579 0.863990 0.000 0.008 0.984 0.000 0.008
#> SRR1071668 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1075804 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1383283 3 0.7210 0.308699 0.000 0.044 0.496 0.244 0.216
#> SRR1350239 3 0.6100 0.529481 0.000 0.020 0.628 0.180 0.172
#> SRR1353878 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1375721 1 0.0771 0.954275 0.976 0.000 0.000 0.004 0.020
#> SRR1083983 1 0.0794 0.946780 0.972 0.000 0.000 0.000 0.028
#> SRR1090095 1 0.0566 0.956717 0.984 0.000 0.000 0.004 0.012
#> SRR1414792 1 0.0671 0.955482 0.980 0.000 0.000 0.004 0.016
#> SRR1075102 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1098737 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1349409 1 0.0865 0.952048 0.972 0.000 0.000 0.004 0.024
#> SRR1413008 3 0.5970 0.554436 0.000 0.020 0.644 0.164 0.172
#> SRR1407179 3 0.1399 0.853671 0.000 0.028 0.952 0.000 0.020
#> SRR1095913 3 0.3844 0.708915 0.000 0.180 0.788 0.004 0.028
#> SRR1403544 1 0.0865 0.952048 0.972 0.000 0.000 0.004 0.024
#> SRR1490546 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR807971 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1436228 5 0.4485 0.846232 0.224 0.036 0.008 0.000 0.732
#> SRR1445218 2 0.0324 0.773119 0.000 0.992 0.000 0.004 0.004
#> SRR1485438 2 0.4235 0.167780 0.000 0.576 0.000 0.000 0.424
#> SRR1358143 1 0.0771 0.954275 0.976 0.000 0.000 0.004 0.020
#> SRR1328760 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1380806 1 0.0451 0.957580 0.988 0.000 0.000 0.004 0.008
#> SRR1379426 3 0.0000 0.866334 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.0162 0.866429 0.000 0.000 0.996 0.000 0.004
#> SRR1086256 5 0.6178 0.762121 0.184 0.076 0.016 0.052 0.672
#> SRR1346734 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1414515 1 0.0771 0.954275 0.976 0.000 0.000 0.004 0.020
#> SRR1082151 5 0.4347 0.848851 0.232 0.032 0.000 0.004 0.732
#> SRR1349320 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1317554 4 0.4444 0.421728 0.000 0.364 0.000 0.624 0.012
#> SRR1076022 2 0.0162 0.772927 0.000 0.996 0.000 0.000 0.004
#> SRR1339573 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1455878 1 0.0404 0.957168 0.988 0.000 0.000 0.000 0.012
#> SRR1446203 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1387397 1 0.0510 0.956193 0.984 0.000 0.000 0.000 0.016
#> SRR1402590 1 0.1831 0.904097 0.920 0.000 0.000 0.004 0.076
#> SRR1317532 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1331488 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1499675 5 0.4217 0.849445 0.232 0.020 0.008 0.000 0.740
#> SRR1440467 3 0.0162 0.866429 0.000 0.000 0.996 0.000 0.004
#> SRR807995 2 0.0162 0.772927 0.000 0.996 0.000 0.000 0.004
#> SRR1476485 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1388214 1 0.0798 0.952904 0.976 0.000 0.000 0.008 0.016
#> SRR1456051 1 0.0671 0.955482 0.980 0.000 0.000 0.004 0.016
#> SRR1473275 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1444083 1 0.0798 0.952904 0.976 0.000 0.000 0.008 0.016
#> SRR1313807 3 0.7116 0.403304 0.000 0.064 0.544 0.208 0.184
#> SRR1470751 5 0.4584 0.841767 0.220 0.032 0.000 0.016 0.732
#> SRR1403434 3 0.0000 0.866334 0.000 0.000 1.000 0.000 0.000
#> SRR1390540 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1093861 2 0.0566 0.773067 0.000 0.984 0.000 0.004 0.012
#> SRR1325290 5 0.4206 0.833897 0.288 0.016 0.000 0.000 0.696
#> SRR1070689 1 0.1831 0.904097 0.920 0.000 0.000 0.004 0.076
#> SRR1384049 1 0.0566 0.957136 0.984 0.000 0.000 0.004 0.012
#> SRR1081184 1 0.1831 0.904097 0.920 0.000 0.000 0.004 0.076
#> SRR1324295 1 0.1831 0.904097 0.920 0.000 0.000 0.004 0.076
#> SRR1365313 5 0.6817 0.656481 0.184 0.060 0.172 0.000 0.584
#> SRR1321877 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR815711 3 0.0290 0.865836 0.000 0.000 0.992 0.000 0.008
#> SRR1433476 3 0.6980 0.120359 0.000 0.024 0.444 0.356 0.176
#> SRR1101883 3 0.0290 0.865836 0.000 0.000 0.992 0.000 0.008
#> SRR1433729 2 0.6465 -0.000579 0.000 0.440 0.000 0.376 0.184
#> SRR1341877 5 0.4114 0.843310 0.272 0.016 0.000 0.000 0.712
#> SRR1090556 5 0.4232 0.810916 0.312 0.012 0.000 0.000 0.676
#> SRR1357389 3 0.0992 0.861945 0.000 0.000 0.968 0.024 0.008
#> SRR1404227 3 0.1469 0.850851 0.000 0.036 0.948 0.000 0.016
#> SRR1376830 1 0.0566 0.958618 0.984 0.000 0.000 0.004 0.012
#> SRR1500661 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1080294 2 0.6288 0.062996 0.000 0.472 0.000 0.372 0.156
#> SRR1336314 4 0.0963 0.903264 0.000 0.036 0.000 0.964 0.000
#> SRR1102152 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1345244 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1478637 5 0.4514 0.805902 0.188 0.072 0.000 0.000 0.740
#> SRR1443776 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1120939 3 0.0510 0.865960 0.000 0.000 0.984 0.000 0.016
#> SRR1080117 3 0.0000 0.866334 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.0771 0.770176 0.000 0.976 0.000 0.004 0.020
#> SRR1091865 1 0.3774 0.383084 0.704 0.000 0.000 0.000 0.296
#> SRR1361072 1 0.0290 0.958858 0.992 0.000 0.000 0.000 0.008
#> SRR1487890 1 0.0865 0.952048 0.972 0.000 0.000 0.004 0.024
#> SRR1349456 3 0.4565 0.506398 0.000 0.308 0.664 0.000 0.028
#> SRR1389384 5 0.3916 0.848582 0.256 0.012 0.000 0.000 0.732
#> SRR1316096 2 0.0162 0.772927 0.000 0.996 0.000 0.000 0.004
#> SRR1408512 1 0.3039 0.666518 0.808 0.000 0.000 0.000 0.192
#> SRR1447547 5 0.7541 -0.103693 0.024 0.020 0.304 0.204 0.448
#> SRR1354053 4 0.4138 0.394251 0.000 0.384 0.000 0.616 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.1829 0.799 0.920 0.004 0.000 0.000 0.012 0.064
#> SRR1349562 1 0.4702 0.726 0.660 0.004 0.000 0.000 0.076 0.260
#> SRR1353376 4 0.0692 0.709 0.000 0.020 0.000 0.976 0.004 0.000
#> SRR1499040 5 0.4145 0.698 0.252 0.000 0.048 0.000 0.700 0.000
#> SRR1322312 1 0.3825 0.761 0.744 0.004 0.000 0.000 0.032 0.220
#> SRR1324412 3 0.0146 0.802 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1100991 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1349479 4 0.5528 0.455 0.000 0.000 0.252 0.556 0.000 0.192
#> SRR1431248 5 0.2006 0.862 0.104 0.004 0.000 0.000 0.892 0.000
#> SRR1405054 3 0.5249 0.349 0.244 0.000 0.600 0.000 0.156 0.000
#> SRR1312266 1 0.0777 0.796 0.972 0.004 0.000 0.000 0.024 0.000
#> SRR1409790 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1352507 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1383763 5 0.4453 0.358 0.372 0.000 0.000 0.000 0.592 0.036
#> SRR1468314 4 0.3894 0.646 0.000 0.220 0.000 0.740 0.004 0.036
#> SRR1473674 2 0.0891 0.971 0.000 0.968 0.000 0.024 0.000 0.008
#> SRR1390499 1 0.4637 0.733 0.672 0.004 0.000 0.000 0.076 0.248
#> SRR821043 4 0.1910 0.695 0.000 0.108 0.000 0.892 0.000 0.000
#> SRR1455653 4 0.1327 0.705 0.000 0.064 0.000 0.936 0.000 0.000
#> SRR1335236 2 0.0862 0.972 0.000 0.972 0.000 0.008 0.004 0.016
#> SRR1095383 4 0.3905 0.649 0.000 0.212 0.000 0.744 0.004 0.040
#> SRR1479489 1 0.2527 0.673 0.832 0.000 0.000 0.000 0.168 0.000
#> SRR1310433 2 0.0291 0.982 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR1073435 4 0.6412 0.433 0.000 0.016 0.236 0.512 0.016 0.220
#> SRR659649 6 0.3578 0.907 0.000 0.000 0.340 0.000 0.000 0.660
#> SRR1395999 1 0.0547 0.797 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1105248 4 0.5546 0.449 0.000 0.000 0.256 0.552 0.000 0.192
#> SRR1338257 1 0.0713 0.795 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1499395 6 0.3804 0.777 0.000 0.000 0.424 0.000 0.000 0.576
#> SRR1350002 2 0.1398 0.949 0.000 0.940 0.000 0.052 0.000 0.008
#> SRR1489757 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1414637 5 0.2282 0.857 0.088 0.024 0.000 0.000 0.888 0.000
#> SRR1478113 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1322477 5 0.1910 0.862 0.108 0.000 0.000 0.000 0.892 0.000
#> SRR1478789 6 0.4119 0.881 0.000 0.016 0.280 0.008 0.004 0.692
#> SRR1414185 6 0.3634 0.888 0.000 0.000 0.356 0.000 0.000 0.644
#> SRR1069141 2 0.0653 0.978 0.000 0.980 0.000 0.004 0.004 0.012
#> SRR1376852 1 0.1075 0.795 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1323491 1 0.0547 0.796 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1338103 5 0.1910 0.862 0.108 0.000 0.000 0.000 0.892 0.000
#> SRR1472012 5 0.2738 0.811 0.176 0.000 0.000 0.000 0.820 0.004
#> SRR1340325 1 0.0632 0.796 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1087321 6 0.3619 0.919 0.000 0.000 0.316 0.004 0.000 0.680
#> SRR1488790 1 0.3727 0.770 0.768 0.004 0.000 0.000 0.040 0.188
#> SRR1334866 5 0.2788 0.846 0.084 0.004 0.012 0.004 0.876 0.020
#> SRR1089446 3 0.3916 0.585 0.036 0.000 0.780 0.008 0.164 0.012
#> SRR1344445 3 0.0146 0.802 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1412969 6 0.3636 0.920 0.000 0.000 0.320 0.004 0.000 0.676
#> SRR1071668 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1075804 1 0.0632 0.797 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1383283 4 0.6143 0.442 0.000 0.016 0.236 0.524 0.004 0.220
#> SRR1350239 4 0.6183 0.156 0.000 0.000 0.404 0.444 0.104 0.048
#> SRR1353878 1 0.0547 0.796 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1375721 1 0.3770 0.763 0.752 0.004 0.000 0.000 0.032 0.212
#> SRR1083983 1 0.3330 0.490 0.716 0.000 0.000 0.000 0.284 0.000
#> SRR1090095 1 0.1615 0.799 0.928 0.004 0.000 0.000 0.004 0.064
#> SRR1414792 1 0.4610 0.732 0.672 0.004 0.000 0.000 0.072 0.252
#> SRR1075102 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1098737 1 0.0865 0.789 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1349409 1 0.4818 0.728 0.664 0.004 0.000 0.000 0.100 0.232
#> SRR1413008 3 0.6700 -0.132 0.008 0.000 0.404 0.392 0.148 0.048
#> SRR1407179 6 0.6932 0.303 0.016 0.016 0.296 0.004 0.292 0.376
#> SRR1095913 6 0.4662 0.810 0.000 0.068 0.228 0.008 0.004 0.692
#> SRR1403544 1 0.4754 0.730 0.668 0.004 0.000 0.000 0.092 0.236
#> SRR1490546 1 0.0547 0.796 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR807971 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1436228 5 0.2020 0.860 0.096 0.000 0.000 0.000 0.896 0.008
#> SRR1445218 2 0.0291 0.982 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1485438 5 0.5379 0.172 0.016 0.396 0.000 0.072 0.516 0.000
#> SRR1358143 1 0.3987 0.757 0.732 0.004 0.000 0.000 0.040 0.224
#> SRR1328760 1 0.2597 0.666 0.824 0.000 0.000 0.000 0.176 0.000
#> SRR1380806 1 0.1668 0.800 0.928 0.004 0.000 0.000 0.008 0.060
#> SRR1379426 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR1087007 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR1086256 5 0.3558 0.718 0.004 0.048 0.012 0.096 0.832 0.008
#> SRR1346734 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1414515 1 0.3825 0.761 0.744 0.004 0.000 0.000 0.032 0.220
#> SRR1082151 5 0.2203 0.856 0.084 0.004 0.000 0.016 0.896 0.000
#> SRR1349320 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1317554 4 0.3370 0.652 0.000 0.212 0.000 0.772 0.004 0.012
#> SRR1076022 2 0.0405 0.982 0.000 0.988 0.000 0.004 0.000 0.008
#> SRR1339573 6 0.3828 0.743 0.000 0.000 0.440 0.000 0.000 0.560
#> SRR1455878 1 0.1387 0.771 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1446203 6 0.3499 0.921 0.000 0.000 0.320 0.000 0.000 0.680
#> SRR1387397 1 0.2762 0.640 0.804 0.000 0.000 0.000 0.196 0.000
#> SRR1402590 1 0.4632 0.730 0.668 0.004 0.000 0.000 0.072 0.256
#> SRR1317532 1 0.2562 0.666 0.828 0.000 0.000 0.000 0.172 0.000
#> SRR1331488 1 0.2562 0.666 0.828 0.000 0.000 0.000 0.172 0.000
#> SRR1499675 5 0.2255 0.856 0.088 0.000 0.000 0.004 0.892 0.016
#> SRR1440467 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR807995 2 0.0520 0.980 0.000 0.984 0.000 0.008 0.000 0.008
#> SRR1476485 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1388214 1 0.2597 0.665 0.824 0.000 0.000 0.000 0.176 0.000
#> SRR1456051 1 0.4610 0.732 0.672 0.004 0.000 0.000 0.072 0.252
#> SRR1473275 3 0.5826 -0.290 0.008 0.000 0.484 0.000 0.152 0.356
#> SRR1444083 1 0.2597 0.665 0.824 0.000 0.000 0.000 0.176 0.000
#> SRR1313807 4 0.6265 0.389 0.000 0.016 0.236 0.496 0.004 0.248
#> SRR1470751 5 0.2342 0.857 0.088 0.004 0.000 0.020 0.888 0.000
#> SRR1403434 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR1390540 1 0.0458 0.796 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1093861 2 0.0551 0.980 0.000 0.984 0.000 0.004 0.004 0.008
#> SRR1325290 5 0.1863 0.862 0.104 0.000 0.000 0.000 0.896 0.000
#> SRR1070689 1 0.4821 0.726 0.660 0.004 0.000 0.000 0.096 0.240
#> SRR1384049 1 0.2649 0.795 0.876 0.004 0.000 0.000 0.068 0.052
#> SRR1081184 1 0.4632 0.730 0.668 0.004 0.000 0.000 0.072 0.256
#> SRR1324295 1 0.4681 0.728 0.664 0.004 0.000 0.000 0.076 0.256
#> SRR1365313 5 0.6413 0.328 0.016 0.016 0.172 0.040 0.608 0.148
#> SRR1321877 6 0.3499 0.921 0.000 0.000 0.320 0.000 0.000 0.680
#> SRR815711 3 0.1732 0.734 0.004 0.000 0.920 0.000 0.072 0.004
#> SRR1433476 4 0.5745 0.438 0.000 0.004 0.256 0.536 0.000 0.204
#> SRR1101883 3 0.0146 0.802 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1433729 4 0.4223 0.659 0.000 0.192 0.000 0.732 0.004 0.072
#> SRR1341877 5 0.1910 0.862 0.108 0.000 0.000 0.000 0.892 0.000
#> SRR1090556 5 0.2300 0.846 0.144 0.000 0.000 0.000 0.856 0.000
#> SRR1357389 3 0.0000 0.805 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1404227 6 0.3848 0.898 0.000 0.012 0.292 0.004 0.000 0.692
#> SRR1376830 1 0.4439 0.743 0.692 0.004 0.000 0.000 0.064 0.240
#> SRR1500661 1 0.0806 0.798 0.972 0.000 0.000 0.000 0.020 0.008
#> SRR1080294 4 0.3933 0.646 0.000 0.216 0.000 0.740 0.004 0.040
#> SRR1336314 4 0.0146 0.709 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1102152 1 0.2597 0.666 0.824 0.000 0.000 0.000 0.176 0.000
#> SRR1345244 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR1478637 5 0.2517 0.849 0.080 0.016 0.000 0.008 0.888 0.008
#> SRR1443776 6 0.3499 0.921 0.000 0.000 0.320 0.000 0.000 0.680
#> SRR1120939 6 0.3499 0.921 0.000 0.000 0.320 0.000 0.000 0.680
#> SRR1080117 6 0.3515 0.921 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR1102899 2 0.0653 0.978 0.000 0.980 0.000 0.004 0.004 0.012
#> SRR1091865 5 0.3409 0.699 0.300 0.000 0.000 0.000 0.700 0.000
#> SRR1361072 1 0.1387 0.770 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1487890 1 0.4754 0.730 0.668 0.004 0.000 0.000 0.092 0.236
#> SRR1349456 6 0.4634 0.816 0.000 0.064 0.232 0.008 0.004 0.692
#> SRR1389384 5 0.1863 0.862 0.104 0.000 0.000 0.000 0.896 0.000
#> SRR1316096 2 0.0405 0.982 0.000 0.988 0.000 0.004 0.000 0.008
#> SRR1408512 5 0.3833 0.385 0.444 0.000 0.000 0.000 0.556 0.000
#> SRR1447547 4 0.7862 0.207 0.028 0.000 0.232 0.388 0.216 0.136
#> SRR1354053 4 0.2912 0.652 0.000 0.216 0.000 0.784 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.959 0.983 0.4971 0.503 0.503
#> 3 3 0.954 0.920 0.967 0.3235 0.755 0.552
#> 4 4 0.926 0.906 0.961 0.0618 0.927 0.798
#> 5 5 0.795 0.781 0.890 0.0752 0.897 0.687
#> 6 6 0.803 0.753 0.866 0.0539 0.944 0.788
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.982 1.000 0.000
#> SRR1349562 1 0.0000 0.982 1.000 0.000
#> SRR1353376 2 0.0000 0.983 0.000 1.000
#> SRR1499040 1 0.0000 0.982 1.000 0.000
#> SRR1322312 1 0.0000 0.982 1.000 0.000
#> SRR1324412 1 0.0000 0.982 1.000 0.000
#> SRR1100991 1 0.0000 0.982 1.000 0.000
#> SRR1349479 2 0.0000 0.983 0.000 1.000
#> SRR1431248 1 0.9427 0.445 0.640 0.360
#> SRR1405054 1 0.0000 0.982 1.000 0.000
#> SRR1312266 1 0.0000 0.982 1.000 0.000
#> SRR1409790 1 0.0000 0.982 1.000 0.000
#> SRR1352507 1 0.0000 0.982 1.000 0.000
#> SRR1383763 1 0.0000 0.982 1.000 0.000
#> SRR1468314 2 0.0000 0.983 0.000 1.000
#> SRR1473674 2 0.0000 0.983 0.000 1.000
#> SRR1390499 1 0.0000 0.982 1.000 0.000
#> SRR821043 2 0.0000 0.983 0.000 1.000
#> SRR1455653 2 0.0000 0.983 0.000 1.000
#> SRR1335236 2 0.0000 0.983 0.000 1.000
#> SRR1095383 2 0.0000 0.983 0.000 1.000
#> SRR1479489 1 0.0000 0.982 1.000 0.000
#> SRR1310433 2 0.0000 0.983 0.000 1.000
#> SRR1073435 2 0.0000 0.983 0.000 1.000
#> SRR659649 2 0.0376 0.980 0.004 0.996
#> SRR1395999 1 0.0000 0.982 1.000 0.000
#> SRR1105248 2 0.0000 0.983 0.000 1.000
#> SRR1338257 1 0.0000 0.982 1.000 0.000
#> SRR1499395 1 0.9754 0.310 0.592 0.408
#> SRR1350002 2 0.0000 0.983 0.000 1.000
#> SRR1489757 1 0.0000 0.982 1.000 0.000
#> SRR1414637 1 0.6048 0.824 0.852 0.148
#> SRR1478113 2 0.0000 0.983 0.000 1.000
#> SRR1322477 1 0.0000 0.982 1.000 0.000
#> SRR1478789 2 0.0000 0.983 0.000 1.000
#> SRR1414185 2 0.0672 0.976 0.008 0.992
#> SRR1069141 2 0.0000 0.983 0.000 1.000
#> SRR1376852 1 0.0000 0.982 1.000 0.000
#> SRR1323491 1 0.0000 0.982 1.000 0.000
#> SRR1338103 1 0.0000 0.982 1.000 0.000
#> SRR1472012 1 0.0000 0.982 1.000 0.000
#> SRR1340325 1 0.0000 0.982 1.000 0.000
#> SRR1087321 2 0.0000 0.983 0.000 1.000
#> SRR1488790 1 0.0000 0.982 1.000 0.000
#> SRR1334866 2 0.0672 0.976 0.008 0.992
#> SRR1089446 1 0.0000 0.982 1.000 0.000
#> SRR1344445 1 0.0000 0.982 1.000 0.000
#> SRR1412969 2 0.0000 0.983 0.000 1.000
#> SRR1071668 1 0.0000 0.982 1.000 0.000
#> SRR1075804 1 0.0000 0.982 1.000 0.000
#> SRR1383283 2 0.0000 0.983 0.000 1.000
#> SRR1350239 2 0.8327 0.640 0.264 0.736
#> SRR1353878 1 0.0000 0.982 1.000 0.000
#> SRR1375721 1 0.0000 0.982 1.000 0.000
#> SRR1083983 1 0.0000 0.982 1.000 0.000
#> SRR1090095 1 0.0000 0.982 1.000 0.000
#> SRR1414792 1 0.0000 0.982 1.000 0.000
#> SRR1075102 2 0.0000 0.983 0.000 1.000
#> SRR1098737 1 0.0000 0.982 1.000 0.000
#> SRR1349409 1 0.0000 0.982 1.000 0.000
#> SRR1413008 2 0.8713 0.587 0.292 0.708
#> SRR1407179 1 0.5842 0.835 0.860 0.140
#> SRR1095913 2 0.0000 0.983 0.000 1.000
#> SRR1403544 1 0.0000 0.982 1.000 0.000
#> SRR1490546 1 0.0000 0.982 1.000 0.000
#> SRR807971 1 0.0000 0.982 1.000 0.000
#> SRR1436228 2 0.2423 0.945 0.040 0.960
#> SRR1445218 2 0.0000 0.983 0.000 1.000
#> SRR1485438 2 0.0000 0.983 0.000 1.000
#> SRR1358143 1 0.0000 0.982 1.000 0.000
#> SRR1328760 1 0.0000 0.982 1.000 0.000
#> SRR1380806 1 0.0000 0.982 1.000 0.000
#> SRR1379426 2 0.0000 0.983 0.000 1.000
#> SRR1087007 2 0.0000 0.983 0.000 1.000
#> SRR1086256 2 0.0000 0.983 0.000 1.000
#> SRR1346734 2 0.0000 0.983 0.000 1.000
#> SRR1414515 1 0.0000 0.982 1.000 0.000
#> SRR1082151 1 0.6623 0.789 0.828 0.172
#> SRR1349320 2 0.0000 0.983 0.000 1.000
#> SRR1317554 2 0.0000 0.983 0.000 1.000
#> SRR1076022 2 0.0000 0.983 0.000 1.000
#> SRR1339573 2 0.9460 0.428 0.364 0.636
#> SRR1455878 1 0.0000 0.982 1.000 0.000
#> SRR1446203 2 0.0000 0.983 0.000 1.000
#> SRR1387397 1 0.0000 0.982 1.000 0.000
#> SRR1402590 1 0.0000 0.982 1.000 0.000
#> SRR1317532 1 0.0000 0.982 1.000 0.000
#> SRR1331488 1 0.0000 0.982 1.000 0.000
#> SRR1499675 1 0.1184 0.969 0.984 0.016
#> SRR1440467 2 0.0000 0.983 0.000 1.000
#> SRR807995 2 0.0000 0.983 0.000 1.000
#> SRR1476485 2 0.0000 0.983 0.000 1.000
#> SRR1388214 1 0.0000 0.982 1.000 0.000
#> SRR1456051 1 0.0000 0.982 1.000 0.000
#> SRR1473275 1 0.0000 0.982 1.000 0.000
#> SRR1444083 1 0.0000 0.982 1.000 0.000
#> SRR1313807 2 0.0000 0.983 0.000 1.000
#> SRR1470751 1 0.0376 0.979 0.996 0.004
#> SRR1403434 2 0.0000 0.983 0.000 1.000
#> SRR1390540 1 0.0000 0.982 1.000 0.000
#> SRR1093861 2 0.0000 0.983 0.000 1.000
#> SRR1325290 1 0.0000 0.982 1.000 0.000
#> SRR1070689 1 0.0000 0.982 1.000 0.000
#> SRR1384049 1 0.0000 0.982 1.000 0.000
#> SRR1081184 1 0.0000 0.982 1.000 0.000
#> SRR1324295 1 0.0000 0.982 1.000 0.000
#> SRR1365313 2 0.0000 0.983 0.000 1.000
#> SRR1321877 2 0.0000 0.983 0.000 1.000
#> SRR815711 1 0.0672 0.976 0.992 0.008
#> SRR1433476 2 0.0000 0.983 0.000 1.000
#> SRR1101883 1 0.1843 0.957 0.972 0.028
#> SRR1433729 2 0.0000 0.983 0.000 1.000
#> SRR1341877 1 0.0000 0.982 1.000 0.000
#> SRR1090556 1 0.0000 0.982 1.000 0.000
#> SRR1357389 1 0.1843 0.958 0.972 0.028
#> SRR1404227 2 0.0000 0.983 0.000 1.000
#> SRR1376830 1 0.0000 0.982 1.000 0.000
#> SRR1500661 1 0.0000 0.982 1.000 0.000
#> SRR1080294 2 0.0000 0.983 0.000 1.000
#> SRR1336314 2 0.0000 0.983 0.000 1.000
#> SRR1102152 1 0.0000 0.982 1.000 0.000
#> SRR1345244 2 0.0000 0.983 0.000 1.000
#> SRR1478637 2 0.0000 0.983 0.000 1.000
#> SRR1443776 2 0.0000 0.983 0.000 1.000
#> SRR1120939 2 0.0000 0.983 0.000 1.000
#> SRR1080117 2 0.0000 0.983 0.000 1.000
#> SRR1102899 2 0.0000 0.983 0.000 1.000
#> SRR1091865 1 0.0000 0.982 1.000 0.000
#> SRR1361072 1 0.0000 0.982 1.000 0.000
#> SRR1487890 1 0.0000 0.982 1.000 0.000
#> SRR1349456 2 0.0000 0.983 0.000 1.000
#> SRR1389384 1 0.0000 0.982 1.000 0.000
#> SRR1316096 2 0.0000 0.983 0.000 1.000
#> SRR1408512 1 0.0000 0.982 1.000 0.000
#> SRR1447547 2 0.0000 0.983 0.000 1.000
#> SRR1354053 2 0.0000 0.983 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1499040 1 0.6244 0.217 0.560 0.000 0.440
#> SRR1322312 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1324412 3 0.1163 0.952 0.028 0.000 0.972
#> SRR1100991 3 0.0592 0.969 0.012 0.000 0.988
#> SRR1349479 3 0.0237 0.973 0.000 0.004 0.996
#> SRR1431248 2 0.6154 0.276 0.408 0.592 0.000
#> SRR1405054 3 0.2356 0.902 0.072 0.000 0.928
#> SRR1312266 1 0.0237 0.970 0.996 0.004 0.000
#> SRR1409790 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1352507 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1383763 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1468314 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1473674 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.973 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1335236 2 0.0592 0.937 0.000 0.988 0.012
#> SRR1095383 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1479489 1 0.2711 0.889 0.912 0.000 0.088
#> SRR1310433 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1073435 2 0.3038 0.858 0.000 0.896 0.104
#> SRR659649 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1105248 3 0.6026 0.346 0.000 0.376 0.624
#> SRR1338257 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1489757 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1414637 1 0.5431 0.607 0.716 0.284 0.000
#> SRR1478113 2 0.0237 0.938 0.004 0.996 0.000
#> SRR1322477 1 0.0424 0.967 0.992 0.008 0.000
#> SRR1478789 3 0.0237 0.973 0.000 0.004 0.996
#> SRR1414185 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1069141 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1376852 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1340325 1 0.0237 0.970 0.996 0.000 0.004
#> SRR1087321 3 0.0237 0.973 0.000 0.004 0.996
#> SRR1488790 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1334866 2 0.2187 0.911 0.028 0.948 0.024
#> SRR1089446 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1344445 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1412969 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1071668 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1075804 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1383283 2 0.1860 0.907 0.000 0.948 0.052
#> SRR1350239 3 0.0475 0.974 0.004 0.004 0.992
#> SRR1353878 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1413008 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1407179 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1095913 2 0.6126 0.352 0.000 0.600 0.400
#> SRR1403544 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.973 1.000 0.000 0.000
#> SRR807971 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1436228 2 0.1289 0.918 0.032 0.968 0.000
#> SRR1445218 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1380806 1 0.0237 0.970 0.996 0.000 0.004
#> SRR1379426 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1082151 1 0.6154 0.311 0.592 0.408 0.000
#> SRR1349320 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1455878 1 0.0237 0.970 0.996 0.000 0.004
#> SRR1446203 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1331488 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1499675 1 0.4399 0.758 0.812 0.000 0.188
#> SRR1440467 3 0.0000 0.975 0.000 0.000 1.000
#> SRR807995 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1473275 3 0.0424 0.972 0.008 0.000 0.992
#> SRR1444083 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1313807 2 0.5291 0.645 0.000 0.732 0.268
#> SRR1470751 1 0.2537 0.898 0.920 0.080 0.000
#> SRR1403434 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1093861 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1325290 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1365313 2 0.5968 0.460 0.000 0.636 0.364
#> SRR1321877 3 0.0000 0.975 0.000 0.000 1.000
#> SRR815711 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1433476 2 0.6045 0.422 0.000 0.620 0.380
#> SRR1101883 3 0.0237 0.975 0.004 0.000 0.996
#> SRR1433729 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1341877 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1090556 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1080294 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1336314 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1102152 1 0.0237 0.970 0.996 0.000 0.004
#> SRR1345244 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1478637 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1443776 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1102899 2 0.0424 0.939 0.000 0.992 0.008
#> SRR1091865 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1349456 3 0.5058 0.657 0.000 0.244 0.756
#> SRR1389384 1 0.0424 0.967 0.992 0.008 0.000
#> SRR1316096 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.941 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.941 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0336 0.8817 0.000 0.008 0.000 0.992
#> SRR1499040 1 0.3450 0.7932 0.836 0.008 0.156 0.000
#> SRR1322312 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1100991 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1349479 3 0.0707 0.9505 0.000 0.000 0.980 0.020
#> SRR1431248 1 0.2805 0.8705 0.888 0.100 0.000 0.012
#> SRR1405054 3 0.0336 0.9594 0.008 0.000 0.992 0.000
#> SRR1312266 1 0.0921 0.9531 0.972 0.000 0.000 0.028
#> SRR1409790 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1468314 2 0.0921 0.9162 0.000 0.972 0.000 0.028
#> SRR1473674 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0188 0.8828 0.000 0.004 0.000 0.996
#> SRR1455653 4 0.4776 0.3737 0.000 0.376 0.000 0.624
#> SRR1335236 2 0.0188 0.9266 0.000 0.996 0.000 0.004
#> SRR1095383 2 0.2921 0.8224 0.000 0.860 0.000 0.140
#> SRR1479489 1 0.0336 0.9685 0.992 0.000 0.008 0.000
#> SRR1310433 2 0.0336 0.9257 0.000 0.992 0.000 0.008
#> SRR1073435 2 0.2861 0.8403 0.000 0.888 0.096 0.016
#> SRR659649 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.3486 0.7629 0.000 0.000 0.188 0.812
#> SRR1338257 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1489757 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1414637 1 0.4543 0.5436 0.676 0.324 0.000 0.000
#> SRR1478113 4 0.0188 0.8828 0.000 0.004 0.000 0.996
#> SRR1322477 1 0.0336 0.9695 0.992 0.008 0.000 0.000
#> SRR1478789 3 0.4643 0.4565 0.000 0.344 0.656 0.000
#> SRR1414185 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0188 0.9266 0.000 0.996 0.000 0.004
#> SRR1376852 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.0336 0.9696 0.992 0.008 0.000 0.000
#> SRR1472012 1 0.0524 0.9674 0.988 0.008 0.004 0.000
#> SRR1340325 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1087321 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.0895 0.9090 0.020 0.976 0.004 0.000
#> SRR1089446 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1344445 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1071668 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1075804 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1383283 2 0.1854 0.8949 0.000 0.940 0.048 0.012
#> SRR1350239 4 0.2216 0.8432 0.000 0.000 0.092 0.908
#> SRR1353878 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.8828 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.2408 0.8357 0.000 0.000 0.104 0.896
#> SRR1407179 3 0.1182 0.9386 0.016 0.016 0.968 0.000
#> SRR1095913 2 0.5163 0.0650 0.000 0.516 0.480 0.004
#> SRR1403544 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR807971 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.0188 0.9242 0.004 0.996 0.000 0.000
#> SRR1445218 2 0.0336 0.9257 0.000 0.992 0.000 0.008
#> SRR1485438 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1358143 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1087007 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1086256 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1346734 4 0.0000 0.8828 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1082151 1 0.3400 0.7815 0.820 0.180 0.000 0.000
#> SRR1349320 4 0.0336 0.8820 0.000 0.008 0.000 0.992
#> SRR1317554 4 0.4989 0.0667 0.000 0.472 0.000 0.528
#> SRR1076022 2 0.0188 0.9266 0.000 0.996 0.000 0.004
#> SRR1339573 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0188 0.9715 0.996 0.000 0.004 0.000
#> SRR1446203 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.0188 0.9715 0.996 0.000 0.004 0.000
#> SRR1402590 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1331488 1 0.4605 0.5011 0.664 0.000 0.000 0.336
#> SRR1499675 1 0.3448 0.7742 0.828 0.000 0.168 0.004
#> SRR1440467 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR807995 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1476485 4 0.0000 0.8828 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1456051 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.0188 0.9718 0.996 0.000 0.000 0.004
#> SRR1313807 3 0.5928 0.0161 0.000 0.456 0.508 0.036
#> SRR1470751 1 0.0592 0.9638 0.984 0.016 0.000 0.000
#> SRR1403434 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1325290 1 0.0336 0.9695 0.992 0.008 0.000 0.000
#> SRR1070689 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.2197 0.8617 0.000 0.916 0.080 0.004
#> SRR1321877 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR815711 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1433476 4 0.3311 0.7795 0.000 0.000 0.172 0.828
#> SRR1101883 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1433729 2 0.3710 0.7549 0.000 0.804 0.004 0.192
#> SRR1341877 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1090556 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1357389 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.0188 0.9657 0.000 0.004 0.996 0.000
#> SRR1376830 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1080294 2 0.2530 0.8517 0.000 0.888 0.000 0.112
#> SRR1336314 4 0.0188 0.8828 0.000 0.004 0.000 0.996
#> SRR1102152 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1345244 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1478637 2 0.0000 0.9265 0.000 1.000 0.000 0.000
#> SRR1443776 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.9692 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0336 0.9257 0.000 0.992 0.000 0.008
#> SRR1091865 1 0.0336 0.9695 0.992 0.008 0.000 0.000
#> SRR1361072 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1487890 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1349456 2 0.1209 0.9092 0.000 0.964 0.032 0.004
#> SRR1389384 1 0.1302 0.9393 0.956 0.044 0.000 0.000
#> SRR1316096 2 0.0188 0.9266 0.000 0.996 0.000 0.004
#> SRR1408512 1 0.0000 0.9741 1.000 0.000 0.000 0.000
#> SRR1447547 4 0.0895 0.8762 0.004 0.020 0.000 0.976
#> SRR1354053 2 0.3569 0.7514 0.000 0.804 0.000 0.196
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.2890 0.7530 0.000 0.160 0.000 0.836 0.004
#> SRR1499040 3 0.6466 -0.0912 0.156 0.004 0.420 0.000 0.420
#> SRR1322312 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0404 0.9349 0.000 0.000 0.988 0.000 0.012
#> SRR1100991 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1349479 3 0.1830 0.9058 0.000 0.040 0.932 0.028 0.000
#> SRR1431248 5 0.6456 0.2916 0.344 0.128 0.000 0.016 0.512
#> SRR1405054 3 0.0566 0.9325 0.012 0.000 0.984 0.000 0.004
#> SRR1312266 1 0.3909 0.7123 0.760 0.000 0.000 0.216 0.024
#> SRR1409790 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1352507 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1383763 1 0.0290 0.9372 0.992 0.000 0.000 0.000 0.008
#> SRR1468314 2 0.3783 0.5982 0.000 0.740 0.000 0.008 0.252
#> SRR1473674 5 0.2127 0.6524 0.000 0.108 0.000 0.000 0.892
#> SRR1390499 1 0.0290 0.9372 0.992 0.000 0.000 0.000 0.008
#> SRR821043 4 0.1845 0.8378 0.000 0.056 0.000 0.928 0.016
#> SRR1455653 4 0.4866 0.5151 0.000 0.052 0.000 0.664 0.284
#> SRR1335236 5 0.4302 -0.0122 0.000 0.480 0.000 0.000 0.520
#> SRR1095383 2 0.3565 0.6727 0.000 0.816 0.000 0.040 0.144
#> SRR1479489 1 0.1153 0.9255 0.964 0.004 0.008 0.000 0.024
#> SRR1310433 2 0.3508 0.5973 0.000 0.748 0.000 0.000 0.252
#> SRR1073435 2 0.1668 0.6846 0.000 0.940 0.032 0.000 0.028
#> SRR659649 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.1357 0.9211 0.948 0.004 0.000 0.000 0.048
#> SRR1105248 4 0.2189 0.8230 0.000 0.012 0.084 0.904 0.000
#> SRR1338257 1 0.3439 0.8449 0.856 0.000 0.040 0.024 0.080
#> SRR1499395 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1350002 5 0.1851 0.6586 0.000 0.088 0.000 0.000 0.912
#> SRR1489757 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1414637 5 0.4763 0.5543 0.212 0.076 0.000 0.000 0.712
#> SRR1478113 4 0.1671 0.8403 0.000 0.000 0.000 0.924 0.076
#> SRR1322477 1 0.1981 0.8931 0.920 0.016 0.000 0.000 0.064
#> SRR1478789 3 0.4425 0.6236 0.000 0.244 0.716 0.000 0.040
#> SRR1414185 3 0.1251 0.9224 0.000 0.036 0.956 0.008 0.000
#> SRR1069141 5 0.3932 0.3931 0.000 0.328 0.000 0.000 0.672
#> SRR1376852 1 0.1195 0.9257 0.960 0.012 0.000 0.000 0.028
#> SRR1323491 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1338103 1 0.4612 0.7133 0.736 0.180 0.000 0.000 0.084
#> SRR1472012 1 0.4035 0.7729 0.784 0.156 0.000 0.000 0.060
#> SRR1340325 1 0.3399 0.7607 0.812 0.000 0.168 0.000 0.020
#> SRR1087321 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1334866 2 0.5346 0.4563 0.168 0.692 0.008 0.000 0.132
#> SRR1089446 3 0.2707 0.8174 0.008 0.132 0.860 0.000 0.000
#> SRR1344445 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1412969 3 0.0162 0.9401 0.000 0.004 0.996 0.000 0.000
#> SRR1071668 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1075804 1 0.2196 0.8972 0.916 0.056 0.000 0.004 0.024
#> SRR1383283 2 0.1153 0.6935 0.000 0.964 0.024 0.004 0.008
#> SRR1350239 4 0.3424 0.6835 0.000 0.000 0.240 0.760 0.000
#> SRR1353878 1 0.0703 0.9312 0.976 0.000 0.000 0.000 0.024
#> SRR1375721 1 0.0324 0.9365 0.992 0.004 0.000 0.000 0.004
#> SRR1083983 1 0.2131 0.9058 0.920 0.008 0.016 0.000 0.056
#> SRR1090095 1 0.0290 0.9372 0.992 0.000 0.000 0.000 0.008
#> SRR1414792 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0510 0.8511 0.000 0.000 0.000 0.984 0.016
#> SRR1098737 1 0.2125 0.9001 0.920 0.052 0.000 0.004 0.024
#> SRR1349409 1 0.0162 0.9380 0.996 0.000 0.000 0.000 0.004
#> SRR1413008 4 0.3480 0.6752 0.000 0.000 0.248 0.752 0.000
#> SRR1407179 2 0.4887 0.4710 0.048 0.692 0.252 0.000 0.008
#> SRR1095913 3 0.5336 0.4503 0.000 0.288 0.628 0.000 0.084
#> SRR1403544 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0290 0.9372 0.992 0.000 0.000 0.000 0.008
#> SRR807971 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1436228 2 0.4595 0.4716 0.172 0.740 0.000 0.000 0.088
#> SRR1445218 2 0.3752 0.5473 0.000 0.708 0.000 0.000 0.292
#> SRR1485438 5 0.2179 0.6490 0.000 0.112 0.000 0.000 0.888
#> SRR1358143 1 0.0162 0.9379 0.996 0.000 0.000 0.000 0.004
#> SRR1328760 1 0.0955 0.9268 0.968 0.004 0.000 0.000 0.028
#> SRR1380806 1 0.0451 0.9352 0.988 0.004 0.000 0.000 0.008
#> SRR1379426 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1087007 3 0.1341 0.9139 0.000 0.056 0.944 0.000 0.000
#> SRR1086256 2 0.3274 0.6106 0.000 0.780 0.000 0.000 0.220
#> SRR1346734 4 0.0404 0.8512 0.000 0.012 0.000 0.988 0.000
#> SRR1414515 1 0.0162 0.9375 0.996 0.004 0.000 0.000 0.000
#> SRR1082151 5 0.1410 0.6623 0.060 0.000 0.000 0.000 0.940
#> SRR1349320 4 0.1484 0.8488 0.000 0.008 0.000 0.944 0.048
#> SRR1317554 2 0.4665 0.6363 0.000 0.740 0.000 0.148 0.112
#> SRR1076022 2 0.3636 0.5714 0.000 0.728 0.000 0.000 0.272
#> SRR1339573 3 0.0162 0.9405 0.000 0.004 0.996 0.000 0.000
#> SRR1455878 1 0.0290 0.9375 0.992 0.000 0.000 0.000 0.008
#> SRR1446203 3 0.0404 0.9377 0.000 0.012 0.988 0.000 0.000
#> SRR1387397 1 0.2573 0.8636 0.880 0.104 0.000 0.000 0.016
#> SRR1402590 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.0693 0.9334 0.980 0.008 0.000 0.000 0.012
#> SRR1331488 1 0.3661 0.6467 0.724 0.000 0.000 0.276 0.000
#> SRR1499675 2 0.5518 0.1524 0.408 0.540 0.024 0.000 0.028
#> SRR1440467 3 0.1544 0.9050 0.000 0.068 0.932 0.000 0.000
#> SRR807995 5 0.1608 0.6607 0.000 0.072 0.000 0.000 0.928
#> SRR1476485 4 0.0404 0.8512 0.000 0.012 0.000 0.988 0.000
#> SRR1388214 1 0.0865 0.9287 0.972 0.004 0.000 0.000 0.024
#> SRR1456051 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1473275 3 0.0290 0.9373 0.000 0.000 0.992 0.000 0.008
#> SRR1444083 1 0.4634 0.7419 0.780 0.000 0.120 0.040 0.060
#> SRR1313807 2 0.1638 0.6823 0.000 0.932 0.064 0.004 0.000
#> SRR1470751 5 0.2411 0.6491 0.108 0.008 0.000 0.000 0.884
#> SRR1403434 3 0.1121 0.9213 0.000 0.044 0.956 0.000 0.000
#> SRR1390540 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1093861 5 0.3816 0.4360 0.000 0.304 0.000 0.000 0.696
#> SRR1325290 1 0.3152 0.8295 0.840 0.024 0.000 0.000 0.136
#> SRR1070689 1 0.0162 0.9379 0.996 0.000 0.000 0.000 0.004
#> SRR1384049 1 0.0290 0.9372 0.992 0.000 0.000 0.000 0.008
#> SRR1081184 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 2 0.1106 0.6961 0.000 0.964 0.012 0.000 0.024
#> SRR1321877 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR815711 3 0.1270 0.9157 0.000 0.052 0.948 0.000 0.000
#> SRR1433476 2 0.5770 0.2356 0.000 0.532 0.096 0.372 0.000
#> SRR1101883 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1433729 2 0.2513 0.7028 0.000 0.904 0.020 0.016 0.060
#> SRR1341877 1 0.3929 0.7362 0.764 0.208 0.000 0.000 0.028
#> SRR1090556 1 0.4318 0.7493 0.764 0.176 0.000 0.004 0.056
#> SRR1357389 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1404227 2 0.3003 0.5728 0.000 0.812 0.188 0.000 0.000
#> SRR1376830 1 0.0000 0.9381 1.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0162 0.9379 0.996 0.000 0.000 0.000 0.004
#> SRR1080294 2 0.3005 0.6886 0.000 0.856 0.008 0.012 0.124
#> SRR1336314 4 0.1831 0.8360 0.000 0.004 0.000 0.920 0.076
#> SRR1102152 1 0.1653 0.9122 0.944 0.004 0.024 0.000 0.028
#> SRR1345244 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 5 0.2329 0.6288 0.000 0.124 0.000 0.000 0.876
#> SRR1443776 3 0.0162 0.9405 0.000 0.004 0.996 0.000 0.000
#> SRR1120939 3 0.1043 0.9243 0.000 0.040 0.960 0.000 0.000
#> SRR1080117 3 0.0000 0.9416 0.000 0.000 1.000 0.000 0.000
#> SRR1102899 2 0.2561 0.6766 0.000 0.856 0.000 0.000 0.144
#> SRR1091865 5 0.3832 0.5365 0.232 0.004 0.004 0.004 0.756
#> SRR1361072 1 0.0162 0.9379 0.996 0.000 0.000 0.000 0.004
#> SRR1487890 1 0.0162 0.9375 0.996 0.004 0.000 0.000 0.000
#> SRR1349456 2 0.2291 0.6972 0.000 0.908 0.056 0.000 0.036
#> SRR1389384 5 0.2605 0.6271 0.148 0.000 0.000 0.000 0.852
#> SRR1316096 5 0.4305 -0.0493 0.000 0.488 0.000 0.000 0.512
#> SRR1408512 1 0.1211 0.9248 0.960 0.024 0.000 0.000 0.016
#> SRR1447547 4 0.3299 0.7897 0.000 0.004 0.016 0.828 0.152
#> SRR1354053 2 0.6517 0.2487 0.000 0.468 0.000 0.212 0.320
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0146 0.9092 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1349562 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 6 0.4366 0.1410 0.000 0.004 0.000 0.440 0.016 0.540
#> SRR1499040 2 0.5600 0.1945 0.036 0.520 0.380 0.000 0.064 0.000
#> SRR1322312 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324412 3 0.0458 0.9111 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1100991 3 0.0692 0.9118 0.000 0.004 0.976 0.000 0.020 0.000
#> SRR1349479 3 0.4011 0.7207 0.000 0.000 0.732 0.028 0.012 0.228
#> SRR1431248 5 0.4088 0.6555 0.012 0.200 0.000 0.044 0.744 0.000
#> SRR1405054 3 0.1232 0.8998 0.024 0.004 0.956 0.000 0.016 0.000
#> SRR1312266 1 0.4566 0.1962 0.520 0.016 0.000 0.452 0.012 0.000
#> SRR1409790 3 0.0508 0.9108 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1352507 3 0.0405 0.9113 0.000 0.004 0.988 0.000 0.008 0.000
#> SRR1383763 1 0.0508 0.9081 0.984 0.000 0.000 0.004 0.012 0.000
#> SRR1468314 6 0.2094 0.6989 0.000 0.080 0.000 0.000 0.020 0.900
#> SRR1473674 2 0.1563 0.7897 0.000 0.932 0.000 0.000 0.012 0.056
#> SRR1390499 1 0.0891 0.9040 0.968 0.000 0.000 0.008 0.024 0.000
#> SRR821043 6 0.3797 0.2395 0.000 0.000 0.000 0.420 0.000 0.580
#> SRR1455653 4 0.4946 0.5014 0.000 0.120 0.000 0.656 0.004 0.220
#> SRR1335236 6 0.4802 0.0154 0.000 0.452 0.000 0.000 0.052 0.496
#> SRR1095383 6 0.0665 0.7020 0.000 0.004 0.000 0.008 0.008 0.980
#> SRR1479489 1 0.1536 0.8881 0.940 0.004 0.016 0.000 0.040 0.000
#> SRR1310433 6 0.1616 0.7064 0.000 0.048 0.000 0.000 0.020 0.932
#> SRR1073435 5 0.3161 0.7034 0.008 0.000 0.028 0.000 0.828 0.136
#> SRR659649 3 0.0363 0.9122 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1395999 1 0.3542 0.7590 0.784 0.016 0.000 0.016 0.184 0.000
#> SRR1105248 4 0.1807 0.8303 0.000 0.000 0.060 0.920 0.000 0.020
#> SRR1338257 1 0.4144 0.8041 0.812 0.056 0.044 0.048 0.040 0.000
#> SRR1499395 3 0.1152 0.9108 0.000 0.000 0.952 0.000 0.044 0.004
#> SRR1350002 2 0.1297 0.7959 0.000 0.948 0.000 0.000 0.012 0.040
#> SRR1489757 3 0.0508 0.9108 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1414637 2 0.1643 0.7787 0.008 0.924 0.000 0.000 0.068 0.000
#> SRR1478113 4 0.1575 0.8410 0.000 0.032 0.000 0.936 0.032 0.000
#> SRR1322477 1 0.2631 0.8275 0.856 0.128 0.000 0.004 0.012 0.000
#> SRR1478789 3 0.5040 0.6125 0.000 0.012 0.652 0.000 0.100 0.236
#> SRR1414185 3 0.2322 0.8895 0.000 0.004 0.896 0.000 0.036 0.064
#> SRR1069141 2 0.4482 0.2622 0.000 0.580 0.000 0.000 0.036 0.384
#> SRR1376852 1 0.2730 0.7738 0.808 0.000 0.000 0.000 0.192 0.000
#> SRR1323491 1 0.0405 0.9085 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR1338103 5 0.2630 0.7605 0.088 0.012 0.000 0.012 0.880 0.008
#> SRR1472012 5 0.3279 0.6862 0.176 0.028 0.000 0.000 0.796 0.000
#> SRR1340325 1 0.3896 0.7566 0.784 0.000 0.136 0.012 0.068 0.000
#> SRR1087321 3 0.1225 0.9115 0.000 0.000 0.952 0.000 0.036 0.012
#> SRR1488790 1 0.0260 0.9089 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1334866 5 0.5952 0.5713 0.132 0.068 0.008 0.000 0.636 0.156
#> SRR1089446 3 0.2985 0.8546 0.028 0.004 0.864 0.000 0.020 0.084
#> SRR1344445 3 0.0260 0.9123 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1412969 3 0.1989 0.8995 0.000 0.004 0.916 0.000 0.052 0.028
#> SRR1071668 3 0.0603 0.9102 0.000 0.004 0.980 0.000 0.016 0.000
#> SRR1075804 1 0.3695 0.6453 0.712 0.000 0.000 0.016 0.272 0.000
#> SRR1383283 6 0.3713 0.5175 0.000 0.004 0.008 0.000 0.284 0.704
#> SRR1350239 4 0.2912 0.6953 0.000 0.000 0.216 0.784 0.000 0.000
#> SRR1353878 1 0.1375 0.9010 0.952 0.008 0.004 0.008 0.028 0.000
#> SRR1375721 1 0.0458 0.9063 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1083983 1 0.3891 0.7501 0.768 0.064 0.004 0.000 0.164 0.000
#> SRR1090095 1 0.0603 0.9070 0.980 0.000 0.000 0.004 0.016 0.000
#> SRR1414792 1 0.0291 0.9092 0.992 0.000 0.000 0.004 0.004 0.000
#> SRR1075102 4 0.0713 0.8443 0.000 0.000 0.000 0.972 0.028 0.000
#> SRR1098737 1 0.4201 0.5584 0.664 0.000 0.000 0.036 0.300 0.000
#> SRR1349409 1 0.0260 0.9095 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1413008 4 0.2871 0.7222 0.000 0.000 0.192 0.804 0.004 0.000
#> SRR1407179 5 0.3142 0.7097 0.008 0.000 0.108 0.000 0.840 0.044
#> SRR1095913 3 0.6546 -0.1692 0.000 0.060 0.404 0.000 0.400 0.136
#> SRR1403544 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 1 0.0891 0.9040 0.968 0.000 0.000 0.008 0.024 0.000
#> SRR807971 3 0.0363 0.9115 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1436228 5 0.3893 0.7128 0.028 0.056 0.000 0.000 0.796 0.120
#> SRR1445218 6 0.2706 0.6852 0.000 0.104 0.000 0.000 0.036 0.860
#> SRR1485438 2 0.1895 0.7776 0.000 0.912 0.000 0.000 0.016 0.072
#> SRR1358143 1 0.0260 0.9095 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1328760 1 0.0893 0.9048 0.972 0.004 0.004 0.004 0.016 0.000
#> SRR1380806 1 0.1074 0.8972 0.960 0.000 0.012 0.000 0.028 0.000
#> SRR1379426 3 0.2173 0.8981 0.000 0.004 0.904 0.000 0.064 0.028
#> SRR1087007 3 0.2389 0.8898 0.000 0.000 0.888 0.000 0.060 0.052
#> SRR1086256 5 0.5067 0.4569 0.000 0.120 0.000 0.000 0.612 0.268
#> SRR1346734 4 0.0790 0.8395 0.000 0.000 0.000 0.968 0.000 0.032
#> SRR1414515 1 0.0363 0.9075 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1082151 2 0.0653 0.7999 0.004 0.980 0.000 0.004 0.000 0.012
#> SRR1349320 4 0.1642 0.8425 0.000 0.028 0.000 0.936 0.032 0.004
#> SRR1317554 6 0.1349 0.6965 0.000 0.004 0.000 0.056 0.000 0.940
#> SRR1076022 6 0.5781 0.0841 0.000 0.176 0.000 0.000 0.396 0.428
#> SRR1339573 3 0.0777 0.9120 0.000 0.000 0.972 0.000 0.024 0.004
#> SRR1455878 1 0.1753 0.8730 0.912 0.004 0.000 0.000 0.084 0.000
#> SRR1446203 3 0.1075 0.9025 0.000 0.000 0.952 0.000 0.048 0.000
#> SRR1387397 1 0.3765 0.3470 0.596 0.000 0.000 0.000 0.404 0.000
#> SRR1402590 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 1 0.1367 0.8944 0.944 0.000 0.000 0.012 0.044 0.000
#> SRR1331488 1 0.1788 0.8716 0.916 0.000 0.000 0.076 0.004 0.004
#> SRR1499675 5 0.2639 0.7602 0.084 0.000 0.008 0.000 0.876 0.032
#> SRR1440467 3 0.4105 0.5244 0.000 0.000 0.632 0.000 0.020 0.348
#> SRR807995 2 0.1176 0.7996 0.000 0.956 0.000 0.000 0.020 0.024
#> SRR1476485 4 0.0790 0.8395 0.000 0.000 0.000 0.968 0.000 0.032
#> SRR1388214 1 0.1536 0.8881 0.940 0.004 0.016 0.000 0.040 0.000
#> SRR1456051 1 0.0260 0.9095 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1473275 3 0.0692 0.9120 0.000 0.004 0.976 0.000 0.020 0.000
#> SRR1444083 1 0.4800 0.6696 0.724 0.008 0.156 0.092 0.020 0.000
#> SRR1313807 6 0.3575 0.4978 0.000 0.000 0.008 0.000 0.284 0.708
#> SRR1470751 2 0.0665 0.7997 0.008 0.980 0.000 0.004 0.000 0.008
#> SRR1403434 3 0.2651 0.8629 0.000 0.000 0.860 0.000 0.028 0.112
#> SRR1390540 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1093861 2 0.4150 0.2865 0.000 0.592 0.000 0.000 0.016 0.392
#> SRR1325290 1 0.5096 0.4469 0.596 0.112 0.000 0.000 0.292 0.000
#> SRR1070689 1 0.0260 0.9089 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1384049 1 0.0405 0.9088 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR1081184 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.4014 0.4097 0.004 0.004 0.004 0.000 0.348 0.640
#> SRR1321877 3 0.1563 0.9078 0.000 0.000 0.932 0.000 0.056 0.012
#> SRR815711 3 0.1059 0.9112 0.000 0.004 0.964 0.000 0.016 0.016
#> SRR1433476 6 0.3429 0.6322 0.000 0.008 0.012 0.128 0.028 0.824
#> SRR1101883 3 0.1152 0.9031 0.000 0.004 0.952 0.000 0.044 0.000
#> SRR1433729 5 0.4608 0.3146 0.000 0.012 0.020 0.004 0.604 0.360
#> SRR1341877 5 0.2809 0.7463 0.128 0.000 0.004 0.000 0.848 0.020
#> SRR1090556 5 0.2876 0.7370 0.132 0.008 0.000 0.016 0.844 0.000
#> SRR1357389 3 0.0508 0.9116 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1404227 5 0.2744 0.7203 0.000 0.000 0.064 0.000 0.864 0.072
#> SRR1376830 1 0.0000 0.9092 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0363 0.9082 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1080294 6 0.0777 0.6992 0.000 0.004 0.000 0.000 0.024 0.972
#> SRR1336314 4 0.1700 0.8321 0.000 0.048 0.000 0.928 0.000 0.024
#> SRR1102152 1 0.1708 0.8830 0.932 0.004 0.024 0.000 0.040 0.000
#> SRR1345244 3 0.1391 0.9077 0.000 0.000 0.944 0.000 0.040 0.016
#> SRR1478637 2 0.3073 0.6473 0.000 0.788 0.000 0.008 0.204 0.000
#> SRR1443776 3 0.1408 0.9096 0.000 0.000 0.944 0.000 0.036 0.020
#> SRR1120939 3 0.1787 0.8846 0.000 0.004 0.920 0.000 0.068 0.008
#> SRR1080117 3 0.1707 0.9063 0.000 0.004 0.928 0.000 0.056 0.012
#> SRR1102899 6 0.1657 0.6997 0.000 0.016 0.000 0.000 0.056 0.928
#> SRR1091865 2 0.1806 0.7661 0.044 0.928 0.000 0.008 0.020 0.000
#> SRR1361072 1 0.0436 0.9096 0.988 0.004 0.000 0.004 0.004 0.000
#> SRR1487890 1 0.0260 0.9083 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1349456 6 0.4131 0.3628 0.000 0.000 0.020 0.000 0.356 0.624
#> SRR1389384 2 0.1148 0.7928 0.020 0.960 0.000 0.000 0.016 0.004
#> SRR1316096 6 0.3098 0.6424 0.000 0.164 0.000 0.000 0.024 0.812
#> SRR1408512 1 0.1556 0.8766 0.920 0.000 0.000 0.000 0.080 0.000
#> SRR1447547 4 0.4174 0.7274 0.000 0.100 0.012 0.764 0.124 0.000
#> SRR1354053 6 0.5923 0.4962 0.000 0.172 0.000 0.196 0.040 0.592
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.967 0.954 0.979 0.3459 0.671 0.671
#> 3 3 0.475 0.631 0.745 0.6438 0.698 0.550
#> 4 4 0.484 0.663 0.801 0.1078 0.819 0.590
#> 5 5 0.539 0.617 0.792 0.0759 0.918 0.768
#> 6 6 0.567 0.651 0.814 0.0440 0.975 0.918
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.976 1.000 0.000
#> SRR1349562 1 0.0000 0.976 1.000 0.000
#> SRR1353376 2 0.0000 0.985 0.000 1.000
#> SRR1499040 1 0.0000 0.976 1.000 0.000
#> SRR1322312 1 0.0000 0.976 1.000 0.000
#> SRR1324412 1 0.0000 0.976 1.000 0.000
#> SRR1100991 1 0.0000 0.976 1.000 0.000
#> SRR1349479 2 0.0000 0.985 0.000 1.000
#> SRR1431248 1 0.1184 0.966 0.984 0.016
#> SRR1405054 1 0.0672 0.972 0.992 0.008
#> SRR1312266 1 0.0000 0.976 1.000 0.000
#> SRR1409790 1 0.0000 0.976 1.000 0.000
#> SRR1352507 1 0.0000 0.976 1.000 0.000
#> SRR1383763 1 0.0000 0.976 1.000 0.000
#> SRR1468314 2 0.0000 0.985 0.000 1.000
#> SRR1473674 1 0.7299 0.755 0.796 0.204
#> SRR1390499 1 0.0000 0.976 1.000 0.000
#> SRR821043 2 0.0000 0.985 0.000 1.000
#> SRR1455653 2 0.0000 0.985 0.000 1.000
#> SRR1335236 1 0.7299 0.755 0.796 0.204
#> SRR1095383 2 0.0000 0.985 0.000 1.000
#> SRR1479489 1 0.0000 0.976 1.000 0.000
#> SRR1310433 2 0.0000 0.985 0.000 1.000
#> SRR1073435 2 0.2603 0.958 0.044 0.956
#> SRR659649 1 0.6623 0.798 0.828 0.172
#> SRR1395999 1 0.0000 0.976 1.000 0.000
#> SRR1105248 1 0.9710 0.368 0.600 0.400
#> SRR1338257 1 0.0000 0.976 1.000 0.000
#> SRR1499395 1 0.0000 0.976 1.000 0.000
#> SRR1350002 1 0.2778 0.937 0.952 0.048
#> SRR1489757 1 0.0000 0.976 1.000 0.000
#> SRR1414637 1 0.0938 0.969 0.988 0.012
#> SRR1478113 2 0.0000 0.985 0.000 1.000
#> SRR1322477 1 0.1184 0.966 0.984 0.016
#> SRR1478789 1 0.0000 0.976 1.000 0.000
#> SRR1414185 1 0.0376 0.974 0.996 0.004
#> SRR1069141 1 0.7299 0.755 0.796 0.204
#> SRR1376852 1 0.0000 0.976 1.000 0.000
#> SRR1323491 1 0.0000 0.976 1.000 0.000
#> SRR1338103 1 0.0000 0.976 1.000 0.000
#> SRR1472012 1 0.0000 0.976 1.000 0.000
#> SRR1340325 1 0.0000 0.976 1.000 0.000
#> SRR1087321 1 0.0000 0.976 1.000 0.000
#> SRR1488790 1 0.0000 0.976 1.000 0.000
#> SRR1334866 1 0.0938 0.969 0.988 0.012
#> SRR1089446 1 0.1184 0.966 0.984 0.016
#> SRR1344445 1 0.0000 0.976 1.000 0.000
#> SRR1412969 1 0.0376 0.974 0.996 0.004
#> SRR1071668 1 0.0672 0.972 0.992 0.008
#> SRR1075804 1 0.0000 0.976 1.000 0.000
#> SRR1383283 2 0.0938 0.981 0.012 0.988
#> SRR1350239 1 0.9710 0.368 0.600 0.400
#> SRR1353878 1 0.0000 0.976 1.000 0.000
#> SRR1375721 1 0.0000 0.976 1.000 0.000
#> SRR1083983 1 0.0000 0.976 1.000 0.000
#> SRR1090095 1 0.0000 0.976 1.000 0.000
#> SRR1414792 1 0.0000 0.976 1.000 0.000
#> SRR1075102 2 0.0000 0.985 0.000 1.000
#> SRR1098737 1 0.0000 0.976 1.000 0.000
#> SRR1349409 1 0.0000 0.976 1.000 0.000
#> SRR1413008 1 0.9710 0.368 0.600 0.400
#> SRR1407179 1 0.0000 0.976 1.000 0.000
#> SRR1095913 1 0.0000 0.976 1.000 0.000
#> SRR1403544 1 0.0000 0.976 1.000 0.000
#> SRR1490546 1 0.0000 0.976 1.000 0.000
#> SRR807971 1 0.0000 0.976 1.000 0.000
#> SRR1436228 1 0.0938 0.969 0.988 0.012
#> SRR1445218 2 0.0938 0.981 0.012 0.988
#> SRR1485438 1 0.0000 0.976 1.000 0.000
#> SRR1358143 1 0.0000 0.976 1.000 0.000
#> SRR1328760 1 0.0000 0.976 1.000 0.000
#> SRR1380806 1 0.0000 0.976 1.000 0.000
#> SRR1379426 1 0.0376 0.974 0.996 0.004
#> SRR1087007 1 0.0376 0.974 0.996 0.004
#> SRR1086256 1 0.0938 0.969 0.988 0.012
#> SRR1346734 2 0.0000 0.985 0.000 1.000
#> SRR1414515 1 0.0000 0.976 1.000 0.000
#> SRR1082151 1 0.0000 0.976 1.000 0.000
#> SRR1349320 2 0.0000 0.985 0.000 1.000
#> SRR1317554 2 0.0000 0.985 0.000 1.000
#> SRR1076022 2 0.1184 0.979 0.016 0.984
#> SRR1339573 1 0.0000 0.976 1.000 0.000
#> SRR1455878 1 0.0000 0.976 1.000 0.000
#> SRR1446203 1 0.6623 0.798 0.828 0.172
#> SRR1387397 1 0.0000 0.976 1.000 0.000
#> SRR1402590 1 0.0000 0.976 1.000 0.000
#> SRR1317532 1 0.0672 0.972 0.992 0.008
#> SRR1331488 1 0.3584 0.919 0.932 0.068
#> SRR1499675 1 0.0000 0.976 1.000 0.000
#> SRR1440467 2 0.3584 0.935 0.068 0.932
#> SRR807995 1 0.0000 0.976 1.000 0.000
#> SRR1476485 2 0.0000 0.985 0.000 1.000
#> SRR1388214 1 0.0376 0.974 0.996 0.004
#> SRR1456051 1 0.0000 0.976 1.000 0.000
#> SRR1473275 1 0.0000 0.976 1.000 0.000
#> SRR1444083 1 0.0000 0.976 1.000 0.000
#> SRR1313807 2 0.0938 0.981 0.012 0.988
#> SRR1470751 1 0.0000 0.976 1.000 0.000
#> SRR1403434 2 0.3584 0.935 0.068 0.932
#> SRR1390540 1 0.0000 0.976 1.000 0.000
#> SRR1093861 2 0.4298 0.911 0.088 0.912
#> SRR1325290 1 0.0000 0.976 1.000 0.000
#> SRR1070689 1 0.0000 0.976 1.000 0.000
#> SRR1384049 1 0.0000 0.976 1.000 0.000
#> SRR1081184 1 0.0000 0.976 1.000 0.000
#> SRR1324295 1 0.0000 0.976 1.000 0.000
#> SRR1365313 1 0.0938 0.969 0.988 0.012
#> SRR1321877 1 0.0000 0.976 1.000 0.000
#> SRR815711 1 0.0376 0.974 0.996 0.004
#> SRR1433476 2 0.0000 0.985 0.000 1.000
#> SRR1101883 1 0.0000 0.976 1.000 0.000
#> SRR1433729 2 0.2603 0.958 0.044 0.956
#> SRR1341877 1 0.0000 0.976 1.000 0.000
#> SRR1090556 1 0.0000 0.976 1.000 0.000
#> SRR1357389 1 0.0000 0.976 1.000 0.000
#> SRR1404227 1 0.0000 0.976 1.000 0.000
#> SRR1376830 1 0.0000 0.976 1.000 0.000
#> SRR1500661 1 0.0000 0.976 1.000 0.000
#> SRR1080294 2 0.0000 0.985 0.000 1.000
#> SRR1336314 2 0.0000 0.985 0.000 1.000
#> SRR1102152 1 0.0000 0.976 1.000 0.000
#> SRR1345244 1 0.0000 0.976 1.000 0.000
#> SRR1478637 1 0.0000 0.976 1.000 0.000
#> SRR1443776 1 0.0000 0.976 1.000 0.000
#> SRR1120939 1 0.0000 0.976 1.000 0.000
#> SRR1080117 1 0.0376 0.974 0.996 0.004
#> SRR1102899 2 0.1184 0.979 0.016 0.984
#> SRR1091865 1 0.0000 0.976 1.000 0.000
#> SRR1361072 1 0.0000 0.976 1.000 0.000
#> SRR1487890 1 0.0000 0.976 1.000 0.000
#> SRR1349456 1 0.0000 0.976 1.000 0.000
#> SRR1389384 1 0.0000 0.976 1.000 0.000
#> SRR1316096 2 0.0000 0.985 0.000 1.000
#> SRR1408512 1 0.0000 0.976 1.000 0.000
#> SRR1447547 1 0.2948 0.934 0.948 0.052
#> SRR1354053 2 0.0000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.6180 0.764 0.584 0.000 0.416
#> SRR1349562 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1353376 2 0.0424 0.929 0.008 0.992 0.000
#> SRR1499040 1 0.5397 0.895 0.720 0.000 0.280
#> SRR1322312 1 0.5397 0.895 0.720 0.000 0.280
#> SRR1324412 3 0.6260 -0.291 0.448 0.000 0.552
#> SRR1100991 3 0.6260 -0.291 0.448 0.000 0.552
#> SRR1349479 2 0.0000 0.929 0.000 1.000 0.000
#> SRR1431248 3 0.0661 0.670 0.008 0.004 0.988
#> SRR1405054 3 0.0237 0.672 0.004 0.000 0.996
#> SRR1312266 1 0.5706 0.902 0.680 0.000 0.320
#> SRR1409790 3 0.6225 -0.228 0.432 0.000 0.568
#> SRR1352507 3 0.5529 0.266 0.296 0.000 0.704
#> SRR1383763 1 0.5397 0.895 0.720 0.000 0.280
#> SRR1468314 2 0.0237 0.928 0.004 0.996 0.000
#> SRR1473674 3 0.5292 0.507 0.228 0.008 0.764
#> SRR1390499 1 0.5529 0.909 0.704 0.000 0.296
#> SRR821043 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1455653 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1335236 3 0.5292 0.507 0.228 0.008 0.764
#> SRR1095383 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1479489 1 0.5733 0.901 0.676 0.000 0.324
#> SRR1310433 2 0.0237 0.928 0.004 0.996 0.000
#> SRR1073435 2 0.6059 0.884 0.188 0.764 0.048
#> SRR659649 3 0.4465 0.551 0.176 0.004 0.820
#> SRR1395999 1 0.5678 0.908 0.684 0.000 0.316
#> SRR1105248 3 0.8568 0.254 0.168 0.228 0.604
#> SRR1338257 1 0.5785 0.895 0.668 0.000 0.332
#> SRR1499395 3 0.5706 0.271 0.320 0.000 0.680
#> SRR1350002 3 0.2774 0.628 0.072 0.008 0.920
#> SRR1489757 3 0.6225 -0.228 0.432 0.000 0.568
#> SRR1414637 3 0.0892 0.671 0.020 0.000 0.980
#> SRR1478113 2 0.3752 0.920 0.144 0.856 0.000
#> SRR1322477 3 0.0661 0.670 0.008 0.004 0.988
#> SRR1478789 3 0.1643 0.671 0.044 0.000 0.956
#> SRR1414185 3 0.0424 0.673 0.008 0.000 0.992
#> SRR1069141 3 0.5292 0.507 0.228 0.008 0.764
#> SRR1376852 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1323491 1 0.6180 0.764 0.584 0.000 0.416
#> SRR1338103 1 0.5882 0.876 0.652 0.000 0.348
#> SRR1472012 1 0.6309 0.502 0.504 0.000 0.496
#> SRR1340325 1 0.5785 0.895 0.668 0.000 0.332
#> SRR1087321 3 0.1643 0.671 0.044 0.000 0.956
#> SRR1488790 1 0.6140 0.788 0.596 0.000 0.404
#> SRR1334866 3 0.0424 0.671 0.008 0.000 0.992
#> SRR1089446 3 0.0661 0.671 0.008 0.004 0.988
#> SRR1344445 3 0.6225 -0.228 0.432 0.000 0.568
#> SRR1412969 3 0.0424 0.673 0.008 0.000 0.992
#> SRR1071668 3 0.0237 0.672 0.004 0.000 0.996
#> SRR1075804 1 0.5706 0.902 0.680 0.000 0.320
#> SRR1383283 2 0.5167 0.897 0.192 0.792 0.016
#> SRR1350239 3 0.8568 0.254 0.168 0.228 0.604
#> SRR1353878 1 0.5785 0.895 0.668 0.000 0.332
#> SRR1375721 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1083983 1 0.6274 0.630 0.544 0.000 0.456
#> SRR1090095 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1414792 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1075102 2 0.3752 0.920 0.144 0.856 0.000
#> SRR1098737 1 0.5706 0.902 0.680 0.000 0.320
#> SRR1349409 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1413008 3 0.8568 0.254 0.168 0.228 0.604
#> SRR1407179 3 0.5465 0.387 0.288 0.000 0.712
#> SRR1095913 3 0.3619 0.613 0.136 0.000 0.864
#> SRR1403544 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1490546 1 0.6305 0.606 0.516 0.000 0.484
#> SRR807971 3 0.5529 0.266 0.296 0.000 0.704
#> SRR1436228 3 0.0424 0.671 0.008 0.000 0.992
#> SRR1445218 2 0.1999 0.925 0.036 0.952 0.012
#> SRR1485438 3 0.3412 0.614 0.124 0.000 0.876
#> SRR1358143 1 0.5397 0.895 0.720 0.000 0.280
#> SRR1328760 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1380806 1 0.5431 0.899 0.716 0.000 0.284
#> SRR1379426 3 0.1031 0.672 0.024 0.000 0.976
#> SRR1087007 3 0.0424 0.673 0.008 0.000 0.992
#> SRR1086256 3 0.0424 0.671 0.008 0.000 0.992
#> SRR1346734 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1414515 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1082151 3 0.3619 0.603 0.136 0.000 0.864
#> SRR1349320 2 0.3752 0.920 0.144 0.856 0.000
#> SRR1317554 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1076022 2 0.5305 0.896 0.192 0.788 0.020
#> SRR1339573 3 0.5859 0.198 0.344 0.000 0.656
#> SRR1455878 1 0.6307 0.537 0.512 0.000 0.488
#> SRR1446203 3 0.4465 0.551 0.176 0.004 0.820
#> SRR1387397 3 0.5431 0.350 0.284 0.000 0.716
#> SRR1402590 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1317532 3 0.6299 -0.456 0.476 0.000 0.524
#> SRR1331488 3 0.2527 0.639 0.044 0.020 0.936
#> SRR1499675 3 0.6192 -0.214 0.420 0.000 0.580
#> SRR1440467 2 0.6710 0.866 0.196 0.732 0.072
#> SRR807995 3 0.3412 0.614 0.124 0.000 0.876
#> SRR1476485 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1388214 3 0.0892 0.672 0.020 0.000 0.980
#> SRR1456051 1 0.5678 0.908 0.684 0.000 0.316
#> SRR1473275 3 0.5859 0.198 0.344 0.000 0.656
#> SRR1444083 1 0.5810 0.891 0.664 0.000 0.336
#> SRR1313807 2 0.5167 0.897 0.192 0.792 0.016
#> SRR1470751 3 0.3619 0.603 0.136 0.000 0.864
#> SRR1403434 2 0.6710 0.866 0.196 0.732 0.072
#> SRR1390540 1 0.6235 0.728 0.564 0.000 0.436
#> SRR1093861 2 0.6756 0.858 0.232 0.712 0.056
#> SRR1325290 3 0.6309 -0.509 0.500 0.000 0.500
#> SRR1070689 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1384049 1 0.5397 0.895 0.720 0.000 0.280
#> SRR1081184 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1324295 1 0.5529 0.909 0.704 0.000 0.296
#> SRR1365313 3 0.0424 0.671 0.008 0.000 0.992
#> SRR1321877 3 0.1643 0.671 0.044 0.000 0.956
#> SRR815711 3 0.0000 0.672 0.000 0.000 1.000
#> SRR1433476 2 0.0000 0.929 0.000 1.000 0.000
#> SRR1101883 3 0.5529 0.266 0.296 0.000 0.704
#> SRR1433729 2 0.6059 0.884 0.188 0.764 0.048
#> SRR1341877 3 0.6252 -0.317 0.444 0.000 0.556
#> SRR1090556 3 0.5431 0.350 0.284 0.000 0.716
#> SRR1357389 3 0.5706 0.271 0.320 0.000 0.680
#> SRR1404227 3 0.4842 0.501 0.224 0.000 0.776
#> SRR1376830 1 0.5650 0.909 0.688 0.000 0.312
#> SRR1500661 3 0.6286 -0.399 0.464 0.000 0.536
#> SRR1080294 2 0.1529 0.925 0.040 0.960 0.000
#> SRR1336314 2 0.3752 0.920 0.144 0.856 0.000
#> SRR1102152 3 0.6204 -0.203 0.424 0.000 0.576
#> SRR1345244 3 0.1643 0.671 0.044 0.000 0.956
#> SRR1478637 3 0.6280 -0.293 0.460 0.000 0.540
#> SRR1443776 3 0.1643 0.671 0.044 0.000 0.956
#> SRR1120939 3 0.2711 0.648 0.088 0.000 0.912
#> SRR1080117 3 0.1031 0.672 0.024 0.000 0.976
#> SRR1102899 2 0.5305 0.896 0.192 0.788 0.020
#> SRR1091865 3 0.5760 0.218 0.328 0.000 0.672
#> SRR1361072 1 0.6305 0.606 0.516 0.000 0.484
#> SRR1487890 1 0.5431 0.899 0.716 0.000 0.284
#> SRR1349456 3 0.1529 0.672 0.040 0.000 0.960
#> SRR1389384 3 0.3619 0.603 0.136 0.000 0.864
#> SRR1316096 2 0.0237 0.928 0.004 0.996 0.000
#> SRR1408512 3 0.6274 -0.370 0.456 0.000 0.544
#> SRR1447547 3 0.1878 0.649 0.044 0.004 0.952
#> SRR1354053 2 0.1529 0.925 0.040 0.960 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.4103 0.6807 0.744 0.000 0.256 0.000
#> SRR1349562 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1353376 2 0.4134 0.6967 0.000 0.740 0.000 0.260
#> SRR1499040 1 0.0804 0.7356 0.980 0.000 0.008 0.012
#> SRR1322312 1 0.0804 0.7356 0.980 0.000 0.008 0.012
#> SRR1324412 1 0.4817 0.4716 0.612 0.000 0.388 0.000
#> SRR1100991 1 0.4817 0.4716 0.612 0.000 0.388 0.000
#> SRR1349479 2 0.4222 0.6878 0.000 0.728 0.000 0.272
#> SRR1431248 3 0.2973 0.7975 0.096 0.020 0.884 0.000
#> SRR1405054 3 0.2466 0.8001 0.096 0.004 0.900 0.000
#> SRR1312266 1 0.2530 0.7811 0.888 0.000 0.112 0.000
#> SRR1409790 1 0.4907 0.4002 0.580 0.000 0.420 0.000
#> SRR1352507 3 0.4866 0.2302 0.404 0.000 0.596 0.000
#> SRR1383763 1 0.0804 0.7356 0.980 0.000 0.008 0.012
#> SRR1468314 2 0.4356 0.6662 0.000 0.708 0.000 0.292
#> SRR1473674 3 0.4323 0.6341 0.000 0.204 0.776 0.020
#> SRR1390499 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR821043 4 0.2345 0.8156 0.000 0.100 0.000 0.900
#> SRR1455653 4 0.2345 0.8156 0.000 0.100 0.000 0.900
#> SRR1335236 3 0.4323 0.6341 0.000 0.204 0.776 0.020
#> SRR1095383 4 0.2868 0.7852 0.000 0.136 0.000 0.864
#> SRR1479489 1 0.2216 0.7847 0.908 0.000 0.092 0.000
#> SRR1310433 2 0.4697 0.5798 0.000 0.644 0.000 0.356
#> SRR1073435 2 0.1452 0.7777 0.000 0.956 0.036 0.008
#> SRR659649 3 0.4151 0.6895 0.016 0.180 0.800 0.004
#> SRR1395999 1 0.2081 0.7856 0.916 0.000 0.084 0.000
#> SRR1105248 3 0.6588 0.4220 0.020 0.248 0.648 0.084
#> SRR1338257 1 0.2647 0.7809 0.880 0.000 0.120 0.000
#> SRR1499395 3 0.4925 0.1574 0.428 0.000 0.572 0.000
#> SRR1350002 3 0.3094 0.7545 0.032 0.048 0.900 0.020
#> SRR1489757 1 0.4907 0.4002 0.580 0.000 0.420 0.000
#> SRR1414637 3 0.2799 0.7990 0.108 0.008 0.884 0.000
#> SRR1478113 4 0.5916 0.6190 0.000 0.272 0.072 0.656
#> SRR1322477 3 0.2973 0.7975 0.096 0.020 0.884 0.000
#> SRR1478789 3 0.2773 0.7951 0.116 0.000 0.880 0.004
#> SRR1414185 3 0.2408 0.7995 0.104 0.000 0.896 0.000
#> SRR1069141 3 0.4323 0.6341 0.000 0.204 0.776 0.020
#> SRR1376852 1 0.1716 0.7825 0.936 0.000 0.064 0.000
#> SRR1323491 1 0.4103 0.6807 0.744 0.000 0.256 0.000
#> SRR1338103 1 0.2921 0.7726 0.860 0.000 0.140 0.000
#> SRR1472012 1 0.4250 0.6459 0.724 0.000 0.276 0.000
#> SRR1340325 1 0.2530 0.7825 0.888 0.000 0.112 0.000
#> SRR1087321 3 0.2773 0.7951 0.116 0.000 0.880 0.004
#> SRR1488790 1 0.3764 0.7188 0.784 0.000 0.216 0.000
#> SRR1334866 3 0.2611 0.7994 0.096 0.008 0.896 0.000
#> SRR1089446 3 0.2741 0.7993 0.096 0.012 0.892 0.000
#> SRR1344445 1 0.4907 0.4002 0.580 0.000 0.420 0.000
#> SRR1412969 3 0.2408 0.7995 0.104 0.000 0.896 0.000
#> SRR1071668 3 0.2466 0.8001 0.096 0.004 0.900 0.000
#> SRR1075804 1 0.2530 0.7811 0.888 0.000 0.112 0.000
#> SRR1383283 2 0.0524 0.7834 0.000 0.988 0.004 0.008
#> SRR1350239 3 0.6588 0.4220 0.020 0.248 0.648 0.084
#> SRR1353878 1 0.2530 0.7825 0.888 0.000 0.112 0.000
#> SRR1375721 1 0.1940 0.7852 0.924 0.000 0.076 0.000
#> SRR1083983 1 0.4103 0.6793 0.744 0.000 0.256 0.000
#> SRR1090095 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1414792 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1075102 4 0.5916 0.6190 0.000 0.272 0.072 0.656
#> SRR1098737 1 0.2530 0.7811 0.888 0.000 0.112 0.000
#> SRR1349409 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1413008 3 0.6588 0.4220 0.020 0.248 0.648 0.084
#> SRR1407179 3 0.4998 0.0100 0.488 0.000 0.512 0.000
#> SRR1095913 3 0.3837 0.6965 0.224 0.000 0.776 0.000
#> SRR1403544 1 0.1940 0.7852 0.924 0.000 0.076 0.000
#> SRR1490546 1 0.4661 0.5513 0.652 0.000 0.348 0.000
#> SRR807971 3 0.4866 0.2302 0.404 0.000 0.596 0.000
#> SRR1436228 3 0.2611 0.7994 0.096 0.008 0.896 0.000
#> SRR1445218 2 0.3764 0.7246 0.000 0.784 0.000 0.216
#> SRR1485438 3 0.4212 0.7208 0.216 0.000 0.772 0.012
#> SRR1358143 1 0.0804 0.7356 0.980 0.000 0.008 0.012
#> SRR1328760 1 0.1940 0.7852 0.924 0.000 0.076 0.000
#> SRR1380806 1 0.0657 0.7561 0.984 0.000 0.012 0.004
#> SRR1379426 3 0.2760 0.7933 0.128 0.000 0.872 0.000
#> SRR1087007 3 0.2408 0.7995 0.104 0.000 0.896 0.000
#> SRR1086256 3 0.2611 0.7994 0.096 0.008 0.896 0.000
#> SRR1346734 4 0.2345 0.8156 0.000 0.100 0.000 0.900
#> SRR1414515 1 0.1940 0.7852 0.924 0.000 0.076 0.000
#> SRR1082151 3 0.4313 0.6749 0.260 0.000 0.736 0.004
#> SRR1349320 4 0.5916 0.6190 0.000 0.272 0.072 0.656
#> SRR1317554 4 0.2345 0.8156 0.000 0.100 0.000 0.900
#> SRR1076022 2 0.0524 0.7835 0.000 0.988 0.004 0.008
#> SRR1339573 1 0.4977 0.1716 0.540 0.000 0.460 0.000
#> SRR1455878 1 0.4250 0.6540 0.724 0.000 0.276 0.000
#> SRR1446203 3 0.4151 0.6895 0.016 0.180 0.800 0.004
#> SRR1387397 3 0.4888 0.2696 0.412 0.000 0.588 0.000
#> SRR1402590 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1317532 1 0.5203 0.3699 0.576 0.008 0.416 0.000
#> SRR1331488 3 0.3977 0.7738 0.084 0.052 0.852 0.012
#> SRR1499675 1 0.4961 0.3265 0.552 0.000 0.448 0.000
#> SRR1440467 2 0.1637 0.7517 0.000 0.940 0.060 0.000
#> SRR807995 3 0.4212 0.7208 0.216 0.000 0.772 0.012
#> SRR1476485 4 0.2345 0.8156 0.000 0.100 0.000 0.900
#> SRR1388214 3 0.3088 0.7935 0.128 0.008 0.864 0.000
#> SRR1456051 1 0.2081 0.7856 0.916 0.000 0.084 0.000
#> SRR1473275 1 0.4977 0.1716 0.540 0.000 0.460 0.000
#> SRR1444083 1 0.2589 0.7816 0.884 0.000 0.116 0.000
#> SRR1313807 2 0.0524 0.7834 0.000 0.988 0.004 0.008
#> SRR1470751 3 0.4313 0.6749 0.260 0.000 0.736 0.004
#> SRR1403434 2 0.1637 0.7517 0.000 0.940 0.060 0.000
#> SRR1390540 1 0.4406 0.6306 0.700 0.000 0.300 0.000
#> SRR1093861 2 0.2256 0.7360 0.000 0.924 0.056 0.020
#> SRR1325290 1 0.4406 0.6229 0.700 0.000 0.300 0.000
#> SRR1070689 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1384049 1 0.0804 0.7356 0.980 0.000 0.008 0.012
#> SRR1081184 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1324295 1 0.1389 0.7787 0.952 0.000 0.048 0.000
#> SRR1365313 3 0.2611 0.7994 0.096 0.008 0.896 0.000
#> SRR1321877 3 0.2773 0.7951 0.116 0.000 0.880 0.004
#> SRR815711 3 0.2281 0.7991 0.096 0.000 0.904 0.000
#> SRR1433476 2 0.4250 0.6844 0.000 0.724 0.000 0.276
#> SRR1101883 3 0.4866 0.2302 0.404 0.000 0.596 0.000
#> SRR1433729 2 0.1452 0.7777 0.000 0.956 0.036 0.008
#> SRR1341877 1 0.4877 0.4361 0.592 0.000 0.408 0.000
#> SRR1090556 3 0.4888 0.2696 0.412 0.000 0.588 0.000
#> SRR1357389 3 0.4898 0.1989 0.416 0.000 0.584 0.000
#> SRR1404227 3 0.4564 0.5020 0.328 0.000 0.672 0.000
#> SRR1376830 1 0.1716 0.7825 0.936 0.000 0.064 0.000
#> SRR1500661 1 0.4830 0.4775 0.608 0.000 0.392 0.000
#> SRR1080294 4 0.2868 0.7852 0.000 0.136 0.000 0.864
#> SRR1336314 4 0.5916 0.6190 0.000 0.272 0.072 0.656
#> SRR1102152 1 0.4941 0.3593 0.564 0.000 0.436 0.000
#> SRR1345244 3 0.2773 0.7951 0.116 0.000 0.880 0.004
#> SRR1478637 1 0.4673 0.5346 0.700 0.000 0.292 0.008
#> SRR1443776 3 0.2773 0.7951 0.116 0.000 0.880 0.004
#> SRR1120939 3 0.3266 0.7578 0.168 0.000 0.832 0.000
#> SRR1080117 3 0.2760 0.7933 0.128 0.000 0.872 0.000
#> SRR1102899 2 0.0524 0.7835 0.000 0.988 0.004 0.008
#> SRR1091865 1 0.5165 0.0898 0.512 0.000 0.484 0.004
#> SRR1361072 1 0.4661 0.5513 0.652 0.000 0.348 0.000
#> SRR1487890 1 0.0657 0.7561 0.984 0.000 0.012 0.004
#> SRR1349456 3 0.2714 0.7963 0.112 0.000 0.884 0.004
#> SRR1389384 3 0.4313 0.6749 0.260 0.000 0.736 0.004
#> SRR1316096 2 0.4697 0.5798 0.000 0.644 0.000 0.356
#> SRR1408512 1 0.4898 0.4214 0.584 0.000 0.416 0.000
#> SRR1447547 3 0.3652 0.7840 0.092 0.052 0.856 0.000
#> SRR1354053 4 0.2345 0.8156 0.000 0.100 0.000 0.900
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.4046 0.6416 0.696 0.000 0.296 0.000 0.008
#> SRR1349562 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1353376 2 0.0771 0.7324 0.000 0.976 0.000 0.020 0.004
#> SRR1499040 1 0.0451 0.7030 0.988 0.000 0.004 0.000 0.008
#> SRR1322312 1 0.0451 0.7030 0.988 0.000 0.004 0.000 0.008
#> SRR1324412 1 0.4287 0.3100 0.540 0.000 0.460 0.000 0.000
#> SRR1100991 1 0.4287 0.3100 0.540 0.000 0.460 0.000 0.000
#> SRR1349479 2 0.0324 0.7265 0.000 0.992 0.000 0.004 0.004
#> SRR1431248 3 0.1356 0.6774 0.012 0.004 0.956 0.000 0.028
#> SRR1405054 3 0.0693 0.6888 0.012 0.000 0.980 0.000 0.008
#> SRR1312266 1 0.2763 0.7690 0.848 0.000 0.148 0.000 0.004
#> SRR1409790 1 0.4307 0.2014 0.504 0.000 0.496 0.000 0.000
#> SRR1352507 3 0.3895 0.3905 0.320 0.000 0.680 0.000 0.000
#> SRR1383763 1 0.0451 0.7030 0.988 0.000 0.004 0.000 0.008
#> SRR1468314 2 0.0865 0.7089 0.000 0.972 0.000 0.004 0.024
#> SRR1473674 5 0.3756 0.6134 0.000 0.008 0.248 0.000 0.744
#> SRR1390499 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR821043 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
#> SRR1455653 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
#> SRR1335236 5 0.3756 0.6134 0.000 0.008 0.248 0.000 0.744
#> SRR1095383 4 0.6186 0.7797 0.000 0.336 0.000 0.512 0.152
#> SRR1479489 1 0.2127 0.7782 0.892 0.000 0.108 0.000 0.000
#> SRR1310433 2 0.2136 0.6363 0.000 0.904 0.000 0.008 0.088
#> SRR1073435 2 0.5205 0.7974 0.000 0.696 0.036 0.040 0.228
#> SRR659649 3 0.3300 0.4319 0.000 0.004 0.792 0.000 0.204
#> SRR1395999 1 0.2020 0.7794 0.900 0.000 0.100 0.000 0.000
#> SRR1105248 3 0.5795 0.1756 0.012 0.008 0.572 0.356 0.052
#> SRR1338257 1 0.2719 0.7744 0.852 0.000 0.144 0.000 0.004
#> SRR1499395 3 0.4151 0.3374 0.344 0.000 0.652 0.000 0.004
#> SRR1350002 5 0.4367 0.6442 0.000 0.004 0.416 0.000 0.580
#> SRR1489757 1 0.4307 0.2014 0.504 0.000 0.496 0.000 0.000
#> SRR1414637 3 0.1211 0.6821 0.024 0.000 0.960 0.000 0.016
#> SRR1478113 4 0.0162 0.6529 0.000 0.004 0.000 0.996 0.000
#> SRR1322477 3 0.1356 0.6774 0.012 0.004 0.956 0.000 0.028
#> SRR1478789 3 0.1281 0.6879 0.032 0.000 0.956 0.000 0.012
#> SRR1414185 3 0.0771 0.6914 0.020 0.000 0.976 0.000 0.004
#> SRR1069141 5 0.3756 0.6134 0.000 0.008 0.248 0.000 0.744
#> SRR1376852 1 0.1732 0.7747 0.920 0.000 0.080 0.000 0.000
#> SRR1323491 1 0.4046 0.6416 0.696 0.000 0.296 0.000 0.008
#> SRR1338103 1 0.3048 0.7610 0.820 0.000 0.176 0.000 0.004
#> SRR1472012 1 0.3837 0.6151 0.692 0.000 0.308 0.000 0.000
#> SRR1340325 1 0.2471 0.7763 0.864 0.000 0.136 0.000 0.000
#> SRR1087321 3 0.1281 0.6879 0.032 0.000 0.956 0.000 0.012
#> SRR1488790 1 0.3715 0.6882 0.736 0.000 0.260 0.000 0.004
#> SRR1334866 3 0.0912 0.6862 0.012 0.000 0.972 0.000 0.016
#> SRR1089446 3 0.1173 0.6816 0.012 0.004 0.964 0.000 0.020
#> SRR1344445 1 0.4307 0.2014 0.504 0.000 0.496 0.000 0.000
#> SRR1412969 3 0.0771 0.6914 0.020 0.000 0.976 0.000 0.004
#> SRR1071668 3 0.0693 0.6888 0.012 0.000 0.980 0.000 0.008
#> SRR1075804 1 0.2763 0.7690 0.848 0.000 0.148 0.000 0.004
#> SRR1383283 2 0.4243 0.8084 0.000 0.712 0.000 0.024 0.264
#> SRR1350239 3 0.5795 0.1756 0.012 0.008 0.572 0.356 0.052
#> SRR1353878 1 0.2471 0.7763 0.864 0.000 0.136 0.000 0.000
#> SRR1375721 1 0.1908 0.7786 0.908 0.000 0.092 0.000 0.000
#> SRR1083983 1 0.3752 0.6499 0.708 0.000 0.292 0.000 0.000
#> SRR1090095 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1414792 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1075102 4 0.0162 0.6529 0.000 0.004 0.000 0.996 0.000
#> SRR1098737 1 0.2763 0.7690 0.848 0.000 0.148 0.000 0.004
#> SRR1349409 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1413008 3 0.5795 0.1756 0.012 0.008 0.572 0.356 0.052
#> SRR1407179 3 0.4256 0.1147 0.436 0.000 0.564 0.000 0.000
#> SRR1095913 3 0.2798 0.6215 0.140 0.000 0.852 0.000 0.008
#> SRR1403544 1 0.1908 0.7786 0.908 0.000 0.092 0.000 0.000
#> SRR1490546 1 0.4455 0.4650 0.588 0.000 0.404 0.000 0.008
#> SRR807971 3 0.3895 0.3905 0.320 0.000 0.680 0.000 0.000
#> SRR1436228 3 0.0912 0.6840 0.012 0.000 0.972 0.000 0.016
#> SRR1445218 2 0.1845 0.7612 0.000 0.928 0.000 0.016 0.056
#> SRR1485438 5 0.6377 0.7292 0.180 0.000 0.336 0.000 0.484
#> SRR1358143 1 0.0451 0.7030 0.988 0.000 0.004 0.000 0.008
#> SRR1328760 1 0.1908 0.7786 0.908 0.000 0.092 0.000 0.000
#> SRR1380806 1 0.0771 0.7317 0.976 0.000 0.020 0.000 0.004
#> SRR1379426 3 0.1282 0.6931 0.044 0.000 0.952 0.000 0.004
#> SRR1087007 3 0.0771 0.6914 0.020 0.000 0.976 0.000 0.004
#> SRR1086256 3 0.0912 0.6840 0.012 0.000 0.972 0.000 0.016
#> SRR1346734 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
#> SRR1414515 1 0.1908 0.7786 0.908 0.000 0.092 0.000 0.000
#> SRR1082151 5 0.6507 0.7116 0.212 0.000 0.316 0.000 0.472
#> SRR1349320 4 0.0162 0.6529 0.000 0.004 0.000 0.996 0.000
#> SRR1317554 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
#> SRR1076022 2 0.4229 0.8085 0.000 0.704 0.000 0.020 0.276
#> SRR1339573 3 0.4300 -0.0166 0.476 0.000 0.524 0.000 0.000
#> SRR1455878 1 0.3949 0.6318 0.696 0.000 0.300 0.000 0.004
#> SRR1446203 3 0.3300 0.4319 0.000 0.004 0.792 0.000 0.204
#> SRR1387397 3 0.4166 0.3467 0.348 0.000 0.648 0.000 0.004
#> SRR1402590 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1317532 1 0.4744 0.2470 0.508 0.000 0.476 0.000 0.016
#> SRR1331488 3 0.2631 0.6230 0.012 0.004 0.904 0.036 0.044
#> SRR1499675 3 0.4450 -0.1947 0.488 0.000 0.508 0.000 0.004
#> SRR1440467 2 0.4597 0.7944 0.000 0.696 0.044 0.000 0.260
#> SRR807995 5 0.6377 0.7292 0.180 0.000 0.336 0.000 0.484
#> SRR1476485 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
#> SRR1388214 3 0.1725 0.6881 0.044 0.000 0.936 0.000 0.020
#> SRR1456051 1 0.2020 0.7794 0.900 0.000 0.100 0.000 0.000
#> SRR1473275 3 0.4300 -0.0166 0.476 0.000 0.524 0.000 0.000
#> SRR1444083 1 0.2516 0.7754 0.860 0.000 0.140 0.000 0.000
#> SRR1313807 2 0.4243 0.8084 0.000 0.712 0.000 0.024 0.264
#> SRR1470751 5 0.6507 0.7116 0.212 0.000 0.316 0.000 0.472
#> SRR1403434 2 0.4597 0.7944 0.000 0.696 0.044 0.000 0.260
#> SRR1390540 1 0.4298 0.5619 0.640 0.000 0.352 0.000 0.008
#> SRR1093861 2 0.4822 0.7716 0.000 0.632 0.016 0.012 0.340
#> SRR1325290 1 0.3966 0.5828 0.664 0.000 0.336 0.000 0.000
#> SRR1070689 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1384049 1 0.0451 0.7030 0.988 0.000 0.004 0.000 0.008
#> SRR1081184 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1324295 1 0.1478 0.7699 0.936 0.000 0.064 0.000 0.000
#> SRR1365313 3 0.0912 0.6840 0.012 0.000 0.972 0.000 0.016
#> SRR1321877 3 0.1281 0.6879 0.032 0.000 0.956 0.000 0.012
#> SRR815711 3 0.0807 0.6870 0.012 0.000 0.976 0.000 0.012
#> SRR1433476 2 0.0451 0.7237 0.000 0.988 0.000 0.004 0.008
#> SRR1101883 3 0.3895 0.3905 0.320 0.000 0.680 0.000 0.000
#> SRR1433729 2 0.5205 0.7974 0.000 0.696 0.036 0.040 0.228
#> SRR1341877 1 0.4546 0.3183 0.532 0.000 0.460 0.000 0.008
#> SRR1090556 3 0.4166 0.3467 0.348 0.000 0.648 0.000 0.004
#> SRR1357389 3 0.4101 0.3655 0.332 0.000 0.664 0.000 0.004
#> SRR1404227 3 0.3607 0.5349 0.244 0.000 0.752 0.000 0.004
#> SRR1376830 1 0.1732 0.7747 0.920 0.000 0.080 0.000 0.000
#> SRR1500661 1 0.4528 0.3689 0.548 0.000 0.444 0.000 0.008
#> SRR1080294 4 0.6186 0.7797 0.000 0.336 0.000 0.512 0.152
#> SRR1336314 4 0.0162 0.6529 0.000 0.004 0.000 0.996 0.000
#> SRR1102152 3 0.4304 -0.1644 0.484 0.000 0.516 0.000 0.000
#> SRR1345244 3 0.1281 0.6879 0.032 0.000 0.956 0.000 0.012
#> SRR1478637 1 0.4201 0.4819 0.664 0.000 0.328 0.000 0.008
#> SRR1443776 3 0.1281 0.6879 0.032 0.000 0.956 0.000 0.012
#> SRR1120939 3 0.2077 0.6610 0.084 0.000 0.908 0.000 0.008
#> SRR1080117 3 0.1282 0.6931 0.044 0.000 0.952 0.000 0.004
#> SRR1102899 2 0.4229 0.8085 0.000 0.704 0.000 0.020 0.276
#> SRR1091865 1 0.6438 0.1661 0.496 0.000 0.292 0.000 0.212
#> SRR1361072 1 0.4455 0.4650 0.588 0.000 0.404 0.000 0.008
#> SRR1487890 1 0.0771 0.7317 0.976 0.000 0.020 0.000 0.004
#> SRR1349456 3 0.1195 0.6878 0.028 0.000 0.960 0.000 0.012
#> SRR1389384 5 0.6507 0.7116 0.212 0.000 0.316 0.000 0.472
#> SRR1316096 2 0.2136 0.6363 0.000 0.904 0.000 0.008 0.088
#> SRR1408512 1 0.4552 0.3009 0.524 0.000 0.468 0.000 0.008
#> SRR1447547 3 0.2229 0.6470 0.012 0.004 0.920 0.012 0.052
#> SRR1354053 4 0.6071 0.8111 0.000 0.300 0.000 0.548 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.3445 0.6571 0.732 0.008 0.260 0.000 0.000 0.000
#> SRR1349562 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1353376 6 0.3575 0.7281 0.000 0.000 0.000 0.284 0.008 0.708
#> SRR1499040 1 0.2509 0.6625 0.876 0.036 0.000 0.000 0.088 0.000
#> SRR1322312 1 0.2509 0.6625 0.876 0.036 0.000 0.000 0.088 0.000
#> SRR1324412 1 0.4457 0.3348 0.544 0.008 0.432 0.000 0.016 0.000
#> SRR1100991 1 0.4457 0.3348 0.544 0.008 0.432 0.000 0.016 0.000
#> SRR1349479 6 0.3409 0.7195 0.000 0.000 0.000 0.300 0.000 0.700
#> SRR1431248 3 0.1138 0.7436 0.004 0.024 0.960 0.000 0.000 0.012
#> SRR1405054 3 0.0603 0.7532 0.004 0.016 0.980 0.000 0.000 0.000
#> SRR1312266 1 0.2165 0.7519 0.884 0.008 0.108 0.000 0.000 0.000
#> SRR1409790 1 0.4654 0.2360 0.504 0.016 0.464 0.000 0.016 0.000
#> SRR1352507 3 0.3619 0.3785 0.316 0.004 0.680 0.000 0.000 0.000
#> SRR1383763 1 0.2509 0.6625 0.876 0.036 0.000 0.000 0.088 0.000
#> SRR1468314 6 0.3499 0.6996 0.000 0.000 0.000 0.320 0.000 0.680
#> SRR1473674 2 0.2134 0.5325 0.000 0.904 0.044 0.000 0.000 0.052
#> SRR1390499 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1455653 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.2134 0.5325 0.000 0.904 0.044 0.000 0.000 0.052
#> SRR1095383 4 0.0937 0.9490 0.000 0.000 0.000 0.960 0.000 0.040
#> SRR1479489 1 0.1644 0.7584 0.920 0.000 0.076 0.000 0.004 0.000
#> SRR1310433 6 0.3852 0.6141 0.000 0.000 0.000 0.384 0.004 0.612
#> SRR1073435 6 0.1693 0.7754 0.000 0.012 0.032 0.000 0.020 0.936
#> SRR659649 3 0.3767 0.5315 0.000 0.260 0.720 0.000 0.004 0.016
#> SRR1395999 1 0.1555 0.7588 0.932 0.004 0.060 0.000 0.004 0.000
#> SRR1105248 3 0.4894 0.2713 0.000 0.040 0.584 0.000 0.360 0.016
#> SRR1338257 1 0.2053 0.7586 0.888 0.004 0.108 0.000 0.000 0.000
#> SRR1499395 3 0.4746 0.3219 0.332 0.036 0.616 0.000 0.016 0.000
#> SRR1350002 2 0.2823 0.6270 0.000 0.796 0.204 0.000 0.000 0.000
#> SRR1489757 1 0.4654 0.2360 0.504 0.016 0.464 0.000 0.016 0.000
#> SRR1414637 3 0.1485 0.7464 0.028 0.024 0.944 0.000 0.004 0.000
#> SRR1478113 5 0.1908 1.0000 0.000 0.000 0.000 0.096 0.900 0.004
#> SRR1322477 3 0.1138 0.7436 0.004 0.024 0.960 0.000 0.000 0.012
#> SRR1478789 3 0.1951 0.7471 0.020 0.060 0.916 0.000 0.004 0.000
#> SRR1414185 3 0.1196 0.7524 0.008 0.040 0.952 0.000 0.000 0.000
#> SRR1069141 2 0.2134 0.5325 0.000 0.904 0.044 0.000 0.000 0.052
#> SRR1376852 1 0.1442 0.7505 0.944 0.012 0.040 0.000 0.004 0.000
#> SRR1323491 1 0.3445 0.6571 0.732 0.008 0.260 0.000 0.000 0.000
#> SRR1338103 1 0.2766 0.7473 0.844 0.008 0.140 0.000 0.008 0.000
#> SRR1472012 1 0.3555 0.6191 0.712 0.000 0.280 0.000 0.008 0.000
#> SRR1340325 1 0.1958 0.7600 0.896 0.004 0.100 0.000 0.000 0.000
#> SRR1087321 3 0.1951 0.7471 0.020 0.060 0.916 0.000 0.004 0.000
#> SRR1488790 1 0.2969 0.6981 0.776 0.000 0.224 0.000 0.000 0.000
#> SRR1334866 3 0.1080 0.7514 0.004 0.032 0.960 0.000 0.004 0.000
#> SRR1089446 3 0.0982 0.7471 0.004 0.020 0.968 0.000 0.004 0.004
#> SRR1344445 1 0.4654 0.2360 0.504 0.016 0.464 0.000 0.016 0.000
#> SRR1412969 3 0.1196 0.7524 0.008 0.040 0.952 0.000 0.000 0.000
#> SRR1071668 3 0.0603 0.7532 0.004 0.016 0.980 0.000 0.000 0.000
#> SRR1075804 1 0.2165 0.7519 0.884 0.008 0.108 0.000 0.000 0.000
#> SRR1383283 6 0.0146 0.7884 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1350239 3 0.4894 0.2713 0.000 0.040 0.584 0.000 0.360 0.016
#> SRR1353878 1 0.1958 0.7600 0.896 0.004 0.100 0.000 0.000 0.000
#> SRR1375721 1 0.1285 0.7577 0.944 0.000 0.052 0.000 0.004 0.000
#> SRR1083983 1 0.3337 0.6560 0.736 0.004 0.260 0.000 0.000 0.000
#> SRR1090095 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1414792 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1075102 5 0.1908 1.0000 0.000 0.000 0.000 0.096 0.900 0.004
#> SRR1098737 1 0.2165 0.7519 0.884 0.008 0.108 0.000 0.000 0.000
#> SRR1349409 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1413008 3 0.4894 0.2713 0.000 0.040 0.584 0.000 0.360 0.016
#> SRR1407179 3 0.4546 0.1343 0.432 0.012 0.540 0.000 0.016 0.000
#> SRR1095913 3 0.3529 0.6892 0.120 0.048 0.816 0.000 0.016 0.000
#> SRR1403544 1 0.1285 0.7577 0.944 0.000 0.052 0.000 0.004 0.000
#> SRR1490546 1 0.3992 0.5126 0.624 0.012 0.364 0.000 0.000 0.000
#> SRR807971 3 0.3619 0.3785 0.316 0.004 0.680 0.000 0.000 0.000
#> SRR1436228 3 0.0922 0.7490 0.004 0.024 0.968 0.000 0.004 0.000
#> SRR1445218 6 0.3109 0.7579 0.000 0.000 0.000 0.224 0.004 0.772
#> SRR1485438 2 0.5796 0.7187 0.124 0.620 0.200 0.000 0.056 0.000
#> SRR1358143 1 0.2509 0.6625 0.876 0.036 0.000 0.000 0.088 0.000
#> SRR1328760 1 0.1285 0.7577 0.944 0.000 0.052 0.000 0.004 0.000
#> SRR1380806 1 0.1769 0.7082 0.924 0.012 0.004 0.000 0.060 0.000
#> SRR1379426 3 0.1713 0.7553 0.028 0.044 0.928 0.000 0.000 0.000
#> SRR1087007 3 0.1196 0.7524 0.008 0.040 0.952 0.000 0.000 0.000
#> SRR1086256 3 0.0922 0.7490 0.004 0.024 0.968 0.000 0.004 0.000
#> SRR1346734 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.1285 0.7577 0.944 0.000 0.052 0.000 0.004 0.000
#> SRR1082151 2 0.5900 0.7067 0.176 0.596 0.188 0.000 0.040 0.000
#> SRR1349320 5 0.1908 1.0000 0.000 0.000 0.000 0.096 0.900 0.004
#> SRR1317554 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1076022 6 0.0436 0.7884 0.000 0.004 0.000 0.004 0.004 0.988
#> SRR1339573 3 0.4756 -0.0369 0.456 0.008 0.504 0.000 0.032 0.000
#> SRR1455878 1 0.3221 0.6508 0.736 0.000 0.264 0.000 0.000 0.000
#> SRR1446203 3 0.3767 0.5315 0.000 0.260 0.720 0.000 0.004 0.016
#> SRR1387397 3 0.4026 0.2706 0.376 0.012 0.612 0.000 0.000 0.000
#> SRR1402590 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1317532 1 0.4229 0.3438 0.548 0.016 0.436 0.000 0.000 0.000
#> SRR1331488 3 0.2152 0.7032 0.000 0.036 0.912 0.000 0.040 0.012
#> SRR1499675 1 0.4410 0.2480 0.508 0.012 0.472 0.000 0.008 0.000
#> SRR1440467 6 0.3422 0.7256 0.000 0.168 0.040 0.000 0.000 0.792
#> SRR807995 2 0.5796 0.7187 0.124 0.620 0.200 0.000 0.056 0.000
#> SRR1476485 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 3 0.2002 0.7403 0.056 0.020 0.916 0.000 0.000 0.008
#> SRR1456051 1 0.1555 0.7588 0.932 0.004 0.060 0.000 0.004 0.000
#> SRR1473275 3 0.4756 -0.0369 0.456 0.008 0.504 0.000 0.032 0.000
#> SRR1444083 1 0.1863 0.7592 0.896 0.000 0.104 0.000 0.000 0.000
#> SRR1313807 6 0.0146 0.7884 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1470751 2 0.5900 0.7067 0.176 0.596 0.188 0.000 0.040 0.000
#> SRR1403434 6 0.3422 0.7256 0.000 0.168 0.040 0.000 0.000 0.792
#> SRR1390540 1 0.3802 0.5929 0.676 0.012 0.312 0.000 0.000 0.000
#> SRR1093861 6 0.1806 0.7465 0.000 0.088 0.000 0.000 0.004 0.908
#> SRR1325290 1 0.3827 0.5888 0.680 0.004 0.308 0.000 0.008 0.000
#> SRR1070689 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1384049 1 0.2509 0.6625 0.876 0.036 0.000 0.000 0.088 0.000
#> SRR1081184 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1324295 1 0.0909 0.7429 0.968 0.012 0.020 0.000 0.000 0.000
#> SRR1365313 3 0.0922 0.7490 0.004 0.024 0.968 0.000 0.004 0.000
#> SRR1321877 3 0.1951 0.7471 0.020 0.060 0.916 0.000 0.004 0.000
#> SRR815711 3 0.0603 0.7509 0.004 0.016 0.980 0.000 0.000 0.000
#> SRR1433476 6 0.3428 0.7161 0.000 0.000 0.000 0.304 0.000 0.696
#> SRR1101883 3 0.3619 0.3785 0.316 0.004 0.680 0.000 0.000 0.000
#> SRR1433729 6 0.1693 0.7754 0.000 0.012 0.032 0.000 0.020 0.936
#> SRR1341877 1 0.4285 0.3774 0.552 0.008 0.432 0.000 0.008 0.000
#> SRR1090556 3 0.4026 0.2706 0.376 0.012 0.612 0.000 0.000 0.000
#> SRR1357389 3 0.4703 0.3505 0.320 0.036 0.628 0.000 0.016 0.000
#> SRR1404227 3 0.4221 0.5623 0.236 0.032 0.716 0.000 0.016 0.000
#> SRR1376830 1 0.1442 0.7505 0.944 0.012 0.040 0.000 0.004 0.000
#> SRR1500661 1 0.4018 0.4319 0.580 0.008 0.412 0.000 0.000 0.000
#> SRR1080294 4 0.0937 0.9490 0.000 0.000 0.000 0.960 0.000 0.040
#> SRR1336314 5 0.1908 1.0000 0.000 0.000 0.000 0.096 0.900 0.004
#> SRR1102152 1 0.4500 0.1946 0.492 0.012 0.484 0.000 0.012 0.000
#> SRR1345244 3 0.1951 0.7471 0.020 0.060 0.916 0.000 0.004 0.000
#> SRR1478637 1 0.4943 0.4366 0.596 0.004 0.328 0.000 0.072 0.000
#> SRR1443776 3 0.1951 0.7471 0.020 0.060 0.916 0.000 0.004 0.000
#> SRR1120939 3 0.2895 0.7256 0.064 0.052 0.868 0.000 0.016 0.000
#> SRR1080117 3 0.1713 0.7553 0.028 0.044 0.928 0.000 0.000 0.000
#> SRR1102899 6 0.0436 0.7884 0.000 0.004 0.000 0.004 0.004 0.988
#> SRR1091865 1 0.6456 0.1410 0.472 0.288 0.204 0.000 0.036 0.000
#> SRR1361072 1 0.3992 0.5126 0.624 0.012 0.364 0.000 0.000 0.000
#> SRR1487890 1 0.1769 0.7082 0.924 0.012 0.004 0.000 0.060 0.000
#> SRR1349456 3 0.1889 0.7477 0.020 0.056 0.920 0.000 0.004 0.000
#> SRR1389384 2 0.5900 0.7067 0.176 0.596 0.188 0.000 0.040 0.000
#> SRR1316096 6 0.3852 0.6141 0.000 0.000 0.000 0.384 0.004 0.612
#> SRR1408512 1 0.4057 0.3793 0.556 0.008 0.436 0.000 0.000 0.000
#> SRR1447547 3 0.1963 0.7242 0.004 0.044 0.924 0.000 0.016 0.012
#> SRR1354053 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.994 0.3689 0.637 0.637
#> 3 3 0.888 0.935 0.971 0.7151 0.644 0.477
#> 4 4 0.618 0.615 0.752 0.1313 0.901 0.735
#> 5 5 0.589 0.468 0.731 0.0726 0.839 0.518
#> 6 6 0.653 0.562 0.710 0.0515 0.872 0.528
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.000 0.993 1.000 0.000
#> SRR1349562 1 0.000 0.993 1.000 0.000
#> SRR1353376 2 0.000 1.000 0.000 1.000
#> SRR1499040 1 0.000 0.993 1.000 0.000
#> SRR1322312 1 0.000 0.993 1.000 0.000
#> SRR1324412 1 0.000 0.993 1.000 0.000
#> SRR1100991 1 0.000 0.993 1.000 0.000
#> SRR1349479 2 0.000 1.000 0.000 1.000
#> SRR1431248 1 0.000 0.993 1.000 0.000
#> SRR1405054 1 0.000 0.993 1.000 0.000
#> SRR1312266 1 0.000 0.993 1.000 0.000
#> SRR1409790 1 0.000 0.993 1.000 0.000
#> SRR1352507 1 0.000 0.993 1.000 0.000
#> SRR1383763 1 0.000 0.993 1.000 0.000
#> SRR1468314 2 0.000 1.000 0.000 1.000
#> SRR1473674 2 0.000 1.000 0.000 1.000
#> SRR1390499 1 0.000 0.993 1.000 0.000
#> SRR821043 2 0.000 1.000 0.000 1.000
#> SRR1455653 2 0.000 1.000 0.000 1.000
#> SRR1335236 1 0.000 0.993 1.000 0.000
#> SRR1095383 2 0.000 1.000 0.000 1.000
#> SRR1479489 1 0.000 0.993 1.000 0.000
#> SRR1310433 2 0.000 1.000 0.000 1.000
#> SRR1073435 2 0.000 1.000 0.000 1.000
#> SRR659649 1 0.000 0.993 1.000 0.000
#> SRR1395999 1 0.000 0.993 1.000 0.000
#> SRR1105248 2 0.000 1.000 0.000 1.000
#> SRR1338257 1 0.000 0.993 1.000 0.000
#> SRR1499395 1 0.000 0.993 1.000 0.000
#> SRR1350002 1 0.000 0.993 1.000 0.000
#> SRR1489757 1 0.000 0.993 1.000 0.000
#> SRR1414637 1 0.000 0.993 1.000 0.000
#> SRR1478113 2 0.000 1.000 0.000 1.000
#> SRR1322477 1 0.000 0.993 1.000 0.000
#> SRR1478789 1 0.000 0.993 1.000 0.000
#> SRR1414185 1 0.000 0.993 1.000 0.000
#> SRR1069141 2 0.000 1.000 0.000 1.000
#> SRR1376852 1 0.000 0.993 1.000 0.000
#> SRR1323491 1 0.000 0.993 1.000 0.000
#> SRR1338103 1 0.000 0.993 1.000 0.000
#> SRR1472012 1 0.000 0.993 1.000 0.000
#> SRR1340325 1 0.000 0.993 1.000 0.000
#> SRR1087321 1 0.000 0.993 1.000 0.000
#> SRR1488790 1 0.000 0.993 1.000 0.000
#> SRR1334866 1 0.000 0.993 1.000 0.000
#> SRR1089446 1 0.000 0.993 1.000 0.000
#> SRR1344445 1 0.000 0.993 1.000 0.000
#> SRR1412969 1 0.000 0.993 1.000 0.000
#> SRR1071668 1 0.000 0.993 1.000 0.000
#> SRR1075804 1 0.000 0.993 1.000 0.000
#> SRR1383283 2 0.000 1.000 0.000 1.000
#> SRR1350239 2 0.000 1.000 0.000 1.000
#> SRR1353878 1 0.000 0.993 1.000 0.000
#> SRR1375721 1 0.000 0.993 1.000 0.000
#> SRR1083983 1 0.000 0.993 1.000 0.000
#> SRR1090095 1 0.000 0.993 1.000 0.000
#> SRR1414792 1 0.000 0.993 1.000 0.000
#> SRR1075102 2 0.000 1.000 0.000 1.000
#> SRR1098737 1 0.000 0.993 1.000 0.000
#> SRR1349409 1 0.000 0.993 1.000 0.000
#> SRR1413008 1 0.929 0.479 0.656 0.344
#> SRR1407179 1 0.000 0.993 1.000 0.000
#> SRR1095913 1 0.000 0.993 1.000 0.000
#> SRR1403544 1 0.000 0.993 1.000 0.000
#> SRR1490546 1 0.000 0.993 1.000 0.000
#> SRR807971 1 0.000 0.993 1.000 0.000
#> SRR1436228 1 0.000 0.993 1.000 0.000
#> SRR1445218 2 0.000 1.000 0.000 1.000
#> SRR1485438 1 0.000 0.993 1.000 0.000
#> SRR1358143 1 0.000 0.993 1.000 0.000
#> SRR1328760 1 0.000 0.993 1.000 0.000
#> SRR1380806 1 0.000 0.993 1.000 0.000
#> SRR1379426 1 0.000 0.993 1.000 0.000
#> SRR1087007 1 0.000 0.993 1.000 0.000
#> SRR1086256 1 0.000 0.993 1.000 0.000
#> SRR1346734 2 0.000 1.000 0.000 1.000
#> SRR1414515 1 0.000 0.993 1.000 0.000
#> SRR1082151 1 0.000 0.993 1.000 0.000
#> SRR1349320 2 0.000 1.000 0.000 1.000
#> SRR1317554 2 0.000 1.000 0.000 1.000
#> SRR1076022 2 0.000 1.000 0.000 1.000
#> SRR1339573 1 0.000 0.993 1.000 0.000
#> SRR1455878 1 0.000 0.993 1.000 0.000
#> SRR1446203 1 0.000 0.993 1.000 0.000
#> SRR1387397 1 0.000 0.993 1.000 0.000
#> SRR1402590 1 0.000 0.993 1.000 0.000
#> SRR1317532 1 0.000 0.993 1.000 0.000
#> SRR1331488 1 0.000 0.993 1.000 0.000
#> SRR1499675 1 0.000 0.993 1.000 0.000
#> SRR1440467 2 0.000 1.000 0.000 1.000
#> SRR807995 1 0.000 0.993 1.000 0.000
#> SRR1476485 2 0.000 1.000 0.000 1.000
#> SRR1388214 1 0.000 0.993 1.000 0.000
#> SRR1456051 1 0.000 0.993 1.000 0.000
#> SRR1473275 1 0.000 0.993 1.000 0.000
#> SRR1444083 1 0.000 0.993 1.000 0.000
#> SRR1313807 2 0.000 1.000 0.000 1.000
#> SRR1470751 1 0.000 0.993 1.000 0.000
#> SRR1403434 2 0.000 1.000 0.000 1.000
#> SRR1390540 1 0.000 0.993 1.000 0.000
#> SRR1093861 2 0.000 1.000 0.000 1.000
#> SRR1325290 1 0.000 0.993 1.000 0.000
#> SRR1070689 1 0.000 0.993 1.000 0.000
#> SRR1384049 1 0.000 0.993 1.000 0.000
#> SRR1081184 1 0.000 0.993 1.000 0.000
#> SRR1324295 1 0.000 0.993 1.000 0.000
#> SRR1365313 1 0.000 0.993 1.000 0.000
#> SRR1321877 1 0.000 0.993 1.000 0.000
#> SRR815711 1 0.000 0.993 1.000 0.000
#> SRR1433476 2 0.000 1.000 0.000 1.000
#> SRR1101883 1 0.000 0.993 1.000 0.000
#> SRR1433729 2 0.000 1.000 0.000 1.000
#> SRR1341877 1 0.000 0.993 1.000 0.000
#> SRR1090556 1 0.000 0.993 1.000 0.000
#> SRR1357389 1 0.000 0.993 1.000 0.000
#> SRR1404227 1 0.000 0.993 1.000 0.000
#> SRR1376830 1 0.000 0.993 1.000 0.000
#> SRR1500661 1 0.000 0.993 1.000 0.000
#> SRR1080294 2 0.000 1.000 0.000 1.000
#> SRR1336314 2 0.000 1.000 0.000 1.000
#> SRR1102152 1 0.000 0.993 1.000 0.000
#> SRR1345244 1 0.000 0.993 1.000 0.000
#> SRR1478637 1 0.000 0.993 1.000 0.000
#> SRR1443776 1 0.000 0.993 1.000 0.000
#> SRR1120939 1 0.000 0.993 1.000 0.000
#> SRR1080117 1 0.000 0.993 1.000 0.000
#> SRR1102899 2 0.000 1.000 0.000 1.000
#> SRR1091865 1 0.000 0.993 1.000 0.000
#> SRR1361072 1 0.000 0.993 1.000 0.000
#> SRR1487890 1 0.000 0.993 1.000 0.000
#> SRR1349456 1 0.000 0.993 1.000 0.000
#> SRR1389384 1 0.000 0.993 1.000 0.000
#> SRR1316096 2 0.000 1.000 0.000 1.000
#> SRR1408512 1 0.000 0.993 1.000 0.000
#> SRR1447547 1 0.975 0.318 0.592 0.408
#> SRR1354053 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1499040 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1322312 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1324412 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1100991 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1349479 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1431248 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1405054 3 0.4346 0.781 0.184 0.000 0.816
#> SRR1312266 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1409790 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1352507 3 0.3116 0.865 0.108 0.000 0.892
#> SRR1383763 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1473674 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1390499 1 0.0000 0.988 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1335236 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1095383 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1073435 3 0.3340 0.847 0.000 0.120 0.880
#> SRR659649 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1105248 3 0.3340 0.847 0.000 0.120 0.880
#> SRR1338257 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1499395 3 0.3482 0.837 0.128 0.000 0.872
#> SRR1350002 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1489757 1 0.0424 0.980 0.992 0.000 0.008
#> SRR1414637 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1478113 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1322477 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1478789 3 0.0892 0.927 0.020 0.000 0.980
#> SRR1414185 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1069141 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1376852 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1334866 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1089446 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1344445 1 0.0424 0.980 0.992 0.000 0.008
#> SRR1412969 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1383283 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1350239 3 0.1163 0.922 0.000 0.028 0.972
#> SRR1353878 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1413008 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1407179 1 0.0424 0.980 0.992 0.000 0.008
#> SRR1095913 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1403544 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1490546 1 0.3116 0.865 0.892 0.000 0.108
#> SRR807971 3 0.1643 0.914 0.044 0.000 0.956
#> SRR1436228 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1445218 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1485438 3 0.4654 0.745 0.208 0.000 0.792
#> SRR1358143 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1086256 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1346734 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1082151 3 0.4654 0.745 0.208 0.000 0.792
#> SRR1349320 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1339573 1 0.0424 0.980 0.992 0.000 0.008
#> SRR1455878 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1317532 3 0.3116 0.865 0.108 0.000 0.892
#> SRR1331488 3 0.3038 0.869 0.104 0.000 0.896
#> SRR1499675 3 0.2537 0.889 0.080 0.000 0.920
#> SRR1440467 3 0.0237 0.934 0.000 0.004 0.996
#> SRR807995 3 0.4654 0.745 0.208 0.000 0.792
#> SRR1476485 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1388214 3 0.3116 0.865 0.108 0.000 0.892
#> SRR1456051 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1473275 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1444083 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1313807 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1470751 3 0.0592 0.931 0.012 0.000 0.988
#> SRR1403434 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1390540 1 0.5058 0.666 0.756 0.000 0.244
#> SRR1093861 2 0.6111 0.345 0.000 0.604 0.396
#> SRR1325290 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1365313 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1321877 3 0.0000 0.937 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1433476 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1101883 3 0.1753 0.911 0.048 0.000 0.952
#> SRR1433729 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1341877 3 0.6305 0.159 0.484 0.000 0.516
#> SRR1090556 3 0.2796 0.879 0.092 0.000 0.908
#> SRR1357389 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1404227 3 0.2356 0.890 0.072 0.000 0.928
#> SRR1376830 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1478637 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1443776 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1361072 3 0.6126 0.380 0.400 0.000 0.600
#> SRR1487890 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1389384 3 0.1529 0.915 0.040 0.000 0.960
#> SRR1316096 2 0.0000 0.982 0.000 1.000 0.000
#> SRR1408512 1 0.4452 0.752 0.808 0.000 0.192
#> SRR1447547 3 0.0000 0.937 0.000 0.000 1.000
#> SRR1354053 2 0.0000 0.982 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.2973 0.65519 0.856 0.144 0.000 0.000
#> SRR1349562 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1353376 4 0.1867 0.95188 0.000 0.072 0.000 0.928
#> SRR1499040 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1322312 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1324412 1 0.0707 0.77090 0.980 0.020 0.000 0.000
#> SRR1100991 1 0.1610 0.75186 0.952 0.032 0.016 0.000
#> SRR1349479 4 0.1716 0.95407 0.000 0.064 0.000 0.936
#> SRR1431248 2 0.7219 0.55399 0.148 0.488 0.364 0.000
#> SRR1405054 2 0.7688 0.63065 0.260 0.456 0.284 0.000
#> SRR1312266 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1409790 1 0.2722 0.71499 0.904 0.032 0.064 0.000
#> SRR1352507 2 0.7705 0.60946 0.244 0.444 0.312 0.000
#> SRR1383763 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1468314 4 0.1557 0.95493 0.000 0.056 0.000 0.944
#> SRR1473674 3 0.3873 0.47421 0.000 0.228 0.772 0.000
#> SRR1390499 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR821043 4 0.0000 0.95141 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.95141 0.000 0.000 0.000 1.000
#> SRR1335236 3 0.2704 0.54000 0.000 0.124 0.876 0.000
#> SRR1095383 4 0.1557 0.95493 0.000 0.056 0.000 0.944
#> SRR1479489 1 0.2469 0.78653 0.892 0.108 0.000 0.000
#> SRR1310433 4 0.1716 0.95379 0.000 0.064 0.000 0.936
#> SRR1073435 2 0.5460 0.26222 0.000 0.632 0.340 0.028
#> SRR659649 3 0.0336 0.56824 0.000 0.008 0.992 0.000
#> SRR1395999 1 0.3444 0.79181 0.816 0.184 0.000 0.000
#> SRR1105248 2 0.5821 0.27920 0.000 0.592 0.368 0.040
#> SRR1338257 1 0.1022 0.75944 0.968 0.032 0.000 0.000
#> SRR1499395 3 0.5168 0.41798 0.248 0.040 0.712 0.000
#> SRR1350002 3 0.2814 0.53649 0.000 0.132 0.868 0.000
#> SRR1489757 1 0.4720 0.48486 0.768 0.044 0.188 0.000
#> SRR1414637 2 0.7416 0.62160 0.244 0.516 0.240 0.000
#> SRR1478113 4 0.2345 0.92329 0.000 0.100 0.000 0.900
#> SRR1322477 3 0.5294 -0.35782 0.008 0.484 0.508 0.000
#> SRR1478789 3 0.5466 0.47585 0.220 0.068 0.712 0.000
#> SRR1414185 3 0.1389 0.54225 0.000 0.048 0.952 0.000
#> SRR1069141 3 0.3873 0.47421 0.000 0.228 0.772 0.000
#> SRR1376852 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1323491 1 0.2973 0.65519 0.856 0.144 0.000 0.000
#> SRR1338103 1 0.1474 0.74921 0.948 0.052 0.000 0.000
#> SRR1472012 1 0.0921 0.75959 0.972 0.028 0.000 0.000
#> SRR1340325 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1087321 3 0.0188 0.57032 0.000 0.004 0.996 0.000
#> SRR1488790 1 0.2973 0.65519 0.856 0.144 0.000 0.000
#> SRR1334866 3 0.7732 -0.50686 0.228 0.384 0.388 0.000
#> SRR1089446 3 0.5000 -0.30921 0.000 0.500 0.500 0.000
#> SRR1344445 1 0.3421 0.67544 0.868 0.044 0.088 0.000
#> SRR1412969 3 0.0469 0.56677 0.000 0.012 0.988 0.000
#> SRR1071668 3 0.4746 -0.13380 0.000 0.368 0.632 0.000
#> SRR1075804 1 0.2921 0.66050 0.860 0.140 0.000 0.000
#> SRR1383283 4 0.3486 0.89737 0.000 0.188 0.000 0.812
#> SRR1350239 2 0.5212 0.33442 0.000 0.572 0.420 0.008
#> SRR1353878 1 0.3975 0.79175 0.760 0.240 0.000 0.000
#> SRR1375721 1 0.0336 0.76742 0.992 0.008 0.000 0.000
#> SRR1083983 1 0.1302 0.75131 0.956 0.044 0.000 0.000
#> SRR1090095 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1414792 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1075102 4 0.0817 0.94612 0.000 0.024 0.000 0.976
#> SRR1098737 1 0.4817 -0.00166 0.612 0.388 0.000 0.000
#> SRR1349409 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1413008 2 0.5212 0.33442 0.000 0.572 0.420 0.008
#> SRR1407179 1 0.2892 0.70462 0.896 0.036 0.068 0.000
#> SRR1095913 3 0.4420 0.45147 0.240 0.012 0.748 0.000
#> SRR1403544 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1490546 1 0.6506 -0.48634 0.468 0.460 0.072 0.000
#> SRR807971 2 0.7705 0.60946 0.244 0.444 0.312 0.000
#> SRR1436228 3 0.5070 -0.18987 0.004 0.416 0.580 0.000
#> SRR1445218 4 0.2921 0.92761 0.000 0.140 0.000 0.860
#> SRR1485438 3 0.6192 0.43884 0.244 0.104 0.652 0.000
#> SRR1358143 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1328760 1 0.1302 0.75131 0.956 0.044 0.000 0.000
#> SRR1380806 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1379426 3 0.5343 0.41063 0.240 0.052 0.708 0.000
#> SRR1087007 3 0.0469 0.56677 0.000 0.012 0.988 0.000
#> SRR1086256 3 0.4985 -0.19653 0.000 0.468 0.532 0.000
#> SRR1346734 4 0.0000 0.95141 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1082151 3 0.6587 0.39855 0.252 0.132 0.616 0.000
#> SRR1349320 4 0.1557 0.94341 0.000 0.056 0.000 0.944
#> SRR1317554 4 0.0000 0.95141 0.000 0.000 0.000 1.000
#> SRR1076022 4 0.2921 0.92761 0.000 0.140 0.000 0.860
#> SRR1339573 1 0.3144 0.73726 0.884 0.044 0.072 0.000
#> SRR1455878 1 0.1474 0.74921 0.948 0.052 0.000 0.000
#> SRR1446203 3 0.0000 0.57014 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.2805 0.69164 0.888 0.100 0.012 0.000
#> SRR1402590 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1317532 2 0.7578 0.63579 0.236 0.480 0.284 0.000
#> SRR1331488 2 0.7058 0.53552 0.136 0.520 0.344 0.000
#> SRR1499675 2 0.7636 0.62972 0.248 0.468 0.284 0.000
#> SRR1440467 3 0.4328 0.42072 0.000 0.244 0.748 0.008
#> SRR807995 3 0.6301 0.42510 0.260 0.104 0.636 0.000
#> SRR1476485 4 0.0000 0.95141 0.000 0.000 0.000 1.000
#> SRR1388214 2 0.7594 0.64064 0.264 0.480 0.256 0.000
#> SRR1456051 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1473275 1 0.2675 0.75654 0.908 0.044 0.048 0.000
#> SRR1444083 1 0.1118 0.75554 0.964 0.036 0.000 0.000
#> SRR1313807 4 0.3486 0.89737 0.000 0.188 0.000 0.812
#> SRR1470751 3 0.7211 0.27804 0.248 0.204 0.548 0.000
#> SRR1403434 3 0.4713 0.26575 0.000 0.360 0.640 0.000
#> SRR1390540 2 0.6924 0.48984 0.428 0.464 0.108 0.000
#> SRR1093861 3 0.7176 0.28951 0.000 0.252 0.552 0.196
#> SRR1325290 1 0.1302 0.75131 0.956 0.044 0.000 0.000
#> SRR1070689 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1384049 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1081184 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1324295 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1365313 3 0.1209 0.56649 0.004 0.032 0.964 0.000
#> SRR1321877 3 0.2773 0.54368 0.116 0.004 0.880 0.000
#> SRR815711 3 0.4936 -0.14863 0.004 0.372 0.624 0.000
#> SRR1433476 4 0.1557 0.95493 0.000 0.056 0.000 0.944
#> SRR1101883 2 0.7705 0.60946 0.244 0.444 0.312 0.000
#> SRR1433729 2 0.4999 -0.00487 0.000 0.508 0.492 0.000
#> SRR1341877 2 0.7631 0.60964 0.320 0.456 0.224 0.000
#> SRR1090556 2 0.7594 0.63917 0.256 0.480 0.264 0.000
#> SRR1357389 3 0.5106 0.42235 0.240 0.040 0.720 0.000
#> SRR1404227 3 0.5021 0.43094 0.240 0.036 0.724 0.000
#> SRR1376830 1 0.4008 0.79203 0.756 0.244 0.000 0.000
#> SRR1500661 1 0.1389 0.75147 0.952 0.048 0.000 0.000
#> SRR1080294 4 0.1557 0.95493 0.000 0.056 0.000 0.944
#> SRR1336314 4 0.0921 0.94640 0.000 0.028 0.000 0.972
#> SRR1102152 1 0.3439 0.66018 0.868 0.048 0.084 0.000
#> SRR1345244 3 0.0000 0.57014 0.000 0.000 1.000 0.000
#> SRR1478637 1 0.2021 0.76763 0.936 0.040 0.024 0.000
#> SRR1443776 3 0.3751 0.49528 0.196 0.004 0.800 0.000
#> SRR1120939 3 0.2271 0.55819 0.076 0.008 0.916 0.000
#> SRR1080117 3 0.5188 0.41695 0.240 0.044 0.716 0.000
#> SRR1102899 4 0.2921 0.92761 0.000 0.140 0.000 0.860
#> SRR1091865 1 0.1302 0.75131 0.956 0.044 0.000 0.000
#> SRR1361072 2 0.7564 0.60370 0.328 0.464 0.208 0.000
#> SRR1487890 1 0.4008 0.79122 0.756 0.244 0.000 0.000
#> SRR1349456 3 0.0188 0.56938 0.000 0.004 0.996 0.000
#> SRR1389384 3 0.6506 0.41109 0.240 0.132 0.628 0.000
#> SRR1316096 4 0.1716 0.95379 0.000 0.064 0.000 0.936
#> SRR1408512 2 0.7231 0.53211 0.392 0.464 0.144 0.000
#> SRR1447547 2 0.4933 0.31728 0.000 0.568 0.432 0.000
#> SRR1354053 4 0.0000 0.95141 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 5 0.4546 -0.14350 0.460 0.000 0.008 0.000 0.532
#> SRR1349562 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4183 0.80185 0.000 0.136 0.000 0.780 0.084
#> SRR1499040 1 0.1197 0.75103 0.952 0.048 0.000 0.000 0.000
#> SRR1322312 1 0.1197 0.75103 0.952 0.048 0.000 0.000 0.000
#> SRR1324412 1 0.5941 0.58885 0.660 0.032 0.128 0.000 0.180
#> SRR1100991 1 0.7132 0.37880 0.484 0.032 0.248 0.000 0.236
#> SRR1349479 4 0.0992 0.84781 0.000 0.024 0.000 0.968 0.008
#> SRR1431248 5 0.4323 0.47789 0.004 0.044 0.200 0.000 0.752
#> SRR1405054 5 0.3660 0.51568 0.016 0.008 0.176 0.000 0.800
#> SRR1312266 1 0.1908 0.73586 0.908 0.000 0.000 0.000 0.092
#> SRR1409790 3 0.7258 0.11048 0.284 0.032 0.448 0.000 0.236
#> SRR1352507 5 0.4589 -0.00323 0.004 0.004 0.472 0.000 0.520
#> SRR1383763 1 0.1197 0.75103 0.952 0.048 0.000 0.000 0.000
#> SRR1468314 4 0.0290 0.85068 0.000 0.008 0.000 0.992 0.000
#> SRR1473674 2 0.4283 0.53347 0.000 0.692 0.292 0.004 0.012
#> SRR1390499 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
#> SRR1455653 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
#> SRR1335236 2 0.4731 0.31807 0.000 0.528 0.456 0.000 0.016
#> SRR1095383 4 0.0162 0.85107 0.000 0.004 0.000 0.996 0.000
#> SRR1479489 1 0.5456 0.62773 0.708 0.032 0.100 0.000 0.160
#> SRR1310433 4 0.0324 0.85120 0.000 0.004 0.000 0.992 0.004
#> SRR1073435 2 0.6633 0.22407 0.000 0.464 0.084 0.044 0.408
#> SRR659649 3 0.3098 0.44088 0.000 0.148 0.836 0.000 0.016
#> SRR1395999 1 0.2536 0.71374 0.868 0.000 0.004 0.000 0.128
#> SRR1105248 5 0.6428 -0.22799 0.000 0.408 0.096 0.024 0.472
#> SRR1338257 1 0.4682 0.36957 0.564 0.000 0.016 0.000 0.420
#> SRR1499395 3 0.2674 0.55182 0.000 0.004 0.856 0.000 0.140
#> SRR1350002 2 0.4731 0.31483 0.000 0.528 0.456 0.000 0.016
#> SRR1489757 3 0.6870 0.22520 0.172 0.032 0.524 0.000 0.272
#> SRR1414637 5 0.3727 0.50991 0.004 0.068 0.104 0.000 0.824
#> SRR1478113 4 0.6482 0.43877 0.000 0.332 0.000 0.468 0.200
#> SRR1322477 5 0.5000 0.40448 0.004 0.068 0.240 0.000 0.688
#> SRR1478789 3 0.2790 0.52207 0.000 0.052 0.880 0.000 0.068
#> SRR1414185 3 0.3319 0.43867 0.000 0.160 0.820 0.000 0.020
#> SRR1069141 2 0.4037 0.53914 0.000 0.704 0.288 0.004 0.004
#> SRR1376852 1 0.0290 0.76266 0.992 0.000 0.000 0.000 0.008
#> SRR1323491 5 0.4546 -0.14350 0.460 0.000 0.008 0.000 0.532
#> SRR1338103 1 0.5297 0.20691 0.476 0.000 0.048 0.000 0.476
#> SRR1472012 1 0.6019 0.37123 0.520 0.012 0.084 0.000 0.384
#> SRR1340325 1 0.1197 0.75701 0.952 0.000 0.000 0.000 0.048
#> SRR1087321 3 0.2011 0.49591 0.000 0.088 0.908 0.000 0.004
#> SRR1488790 5 0.4546 -0.14350 0.460 0.000 0.008 0.000 0.532
#> SRR1334866 3 0.5360 0.21318 0.000 0.060 0.556 0.000 0.384
#> SRR1089446 5 0.6718 -0.19174 0.000 0.328 0.260 0.000 0.412
#> SRR1344445 3 0.7191 0.16061 0.232 0.032 0.468 0.000 0.268
#> SRR1412969 3 0.3061 0.45313 0.000 0.136 0.844 0.000 0.020
#> SRR1071668 3 0.5210 0.30389 0.000 0.132 0.684 0.000 0.184
#> SRR1075804 5 0.4449 -0.19233 0.484 0.000 0.004 0.000 0.512
#> SRR1383283 4 0.4736 0.56091 0.000 0.312 0.004 0.656 0.028
#> SRR1350239 5 0.5979 -0.10196 0.000 0.360 0.120 0.000 0.520
#> SRR1353878 1 0.1012 0.76296 0.968 0.012 0.000 0.000 0.020
#> SRR1375721 1 0.5653 0.53587 0.632 0.012 0.088 0.000 0.268
#> SRR1083983 1 0.6164 0.22373 0.448 0.012 0.092 0.000 0.448
#> SRR1090095 1 0.0510 0.76116 0.984 0.000 0.000 0.000 0.016
#> SRR1414792 1 0.0404 0.76195 0.988 0.000 0.000 0.000 0.012
#> SRR1075102 4 0.4035 0.80887 0.000 0.156 0.000 0.784 0.060
#> SRR1098737 5 0.3585 0.42080 0.220 0.004 0.004 0.000 0.772
#> SRR1349409 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1413008 5 0.5979 -0.10196 0.000 0.360 0.120 0.000 0.520
#> SRR1407179 3 0.7299 0.09660 0.288 0.032 0.436 0.000 0.244
#> SRR1095913 3 0.2674 0.55093 0.000 0.012 0.868 0.000 0.120
#> SRR1403544 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1490546 5 0.3717 0.53054 0.144 0.012 0.028 0.000 0.816
#> SRR807971 3 0.4449 0.04739 0.000 0.004 0.512 0.000 0.484
#> SRR1436228 5 0.5953 0.15937 0.000 0.124 0.336 0.000 0.540
#> SRR1445218 4 0.2773 0.77183 0.000 0.164 0.000 0.836 0.000
#> SRR1485438 3 0.5843 0.21206 0.000 0.304 0.572 0.000 0.124
#> SRR1358143 1 0.1197 0.75103 0.952 0.048 0.000 0.000 0.000
#> SRR1328760 1 0.6069 0.30017 0.484 0.012 0.084 0.000 0.420
#> SRR1380806 1 0.0963 0.75593 0.964 0.036 0.000 0.000 0.000
#> SRR1379426 3 0.2605 0.55081 0.000 0.000 0.852 0.000 0.148
#> SRR1087007 3 0.2921 0.46249 0.000 0.124 0.856 0.000 0.020
#> SRR1086256 5 0.6667 -0.22435 0.000 0.364 0.232 0.000 0.404
#> SRR1346734 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
#> SRR1414515 1 0.1205 0.75960 0.956 0.004 0.000 0.000 0.040
#> SRR1082151 3 0.6172 0.24462 0.000 0.280 0.544 0.000 0.176
#> SRR1349320 4 0.4647 0.78822 0.000 0.172 0.000 0.736 0.092
#> SRR1317554 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
#> SRR1076022 4 0.2773 0.77183 0.000 0.164 0.000 0.836 0.000
#> SRR1339573 3 0.7110 0.08990 0.328 0.036 0.464 0.000 0.172
#> SRR1455878 5 0.5353 -0.24883 0.472 0.000 0.052 0.000 0.476
#> SRR1446203 3 0.2763 0.43803 0.000 0.148 0.848 0.000 0.004
#> SRR1387397 5 0.6192 0.03248 0.348 0.012 0.108 0.000 0.532
#> SRR1402590 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.4196 0.50648 0.012 0.036 0.176 0.000 0.776
#> SRR1331488 5 0.4343 0.36313 0.000 0.136 0.096 0.000 0.768
#> SRR1499675 5 0.3167 0.51648 0.008 0.008 0.148 0.000 0.836
#> SRR1440467 2 0.7185 0.48325 0.000 0.488 0.324 0.112 0.076
#> SRR807995 3 0.6100 0.21431 0.004 0.304 0.556 0.000 0.136
#> SRR1476485 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
#> SRR1388214 5 0.3883 0.52275 0.012 0.032 0.152 0.000 0.804
#> SRR1456051 1 0.1341 0.75496 0.944 0.000 0.000 0.000 0.056
#> SRR1473275 1 0.7155 0.16903 0.416 0.032 0.372 0.000 0.180
#> SRR1444083 1 0.6064 0.36384 0.516 0.012 0.088 0.000 0.384
#> SRR1313807 4 0.4736 0.56091 0.000 0.312 0.004 0.656 0.028
#> SRR1470751 5 0.6909 0.00691 0.008 0.268 0.288 0.000 0.436
#> SRR1403434 2 0.7354 0.47042 0.000 0.476 0.320 0.100 0.104
#> SRR1390540 5 0.3730 0.53750 0.112 0.012 0.048 0.000 0.828
#> SRR1093861 2 0.5183 0.49698 0.000 0.692 0.104 0.200 0.004
#> SRR1325290 5 0.5904 -0.24963 0.452 0.012 0.068 0.000 0.468
#> SRR1070689 1 0.0162 0.76054 0.996 0.000 0.000 0.000 0.004
#> SRR1384049 1 0.1197 0.75103 0.952 0.048 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.76169 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.3994 0.39841 0.000 0.140 0.792 0.000 0.068
#> SRR1321877 3 0.2046 0.51607 0.000 0.068 0.916 0.000 0.016
#> SRR815711 3 0.5516 0.24820 0.000 0.128 0.640 0.000 0.232
#> SRR1433476 4 0.0992 0.84781 0.000 0.024 0.000 0.968 0.008
#> SRR1101883 5 0.4415 0.08567 0.000 0.004 0.444 0.000 0.552
#> SRR1433729 2 0.7175 0.45806 0.000 0.528 0.112 0.092 0.268
#> SRR1341877 5 0.3051 0.52398 0.028 0.000 0.120 0.000 0.852
#> SRR1090556 5 0.3443 0.51671 0.008 0.012 0.164 0.000 0.816
#> SRR1357389 3 0.1732 0.55342 0.000 0.000 0.920 0.000 0.080
#> SRR1404227 3 0.2891 0.53795 0.000 0.000 0.824 0.000 0.176
#> SRR1376830 1 0.0290 0.76266 0.992 0.000 0.000 0.000 0.008
#> SRR1500661 1 0.5296 0.23213 0.484 0.000 0.048 0.000 0.468
#> SRR1080294 4 0.0324 0.85120 0.000 0.004 0.000 0.992 0.004
#> SRR1336314 4 0.4170 0.80281 0.000 0.140 0.000 0.780 0.080
#> SRR1102152 1 0.7442 0.16846 0.376 0.032 0.280 0.000 0.312
#> SRR1345244 3 0.2583 0.45672 0.000 0.132 0.864 0.000 0.004
#> SRR1478637 1 0.6540 0.53770 0.600 0.040 0.180 0.000 0.180
#> SRR1443776 3 0.1626 0.52587 0.000 0.044 0.940 0.000 0.016
#> SRR1120939 3 0.1764 0.51153 0.000 0.064 0.928 0.000 0.008
#> SRR1080117 3 0.2516 0.55268 0.000 0.000 0.860 0.000 0.140
#> SRR1102899 4 0.2773 0.77183 0.000 0.164 0.000 0.836 0.000
#> SRR1091865 1 0.6476 0.31127 0.472 0.012 0.132 0.000 0.384
#> SRR1361072 5 0.3940 0.53739 0.044 0.012 0.136 0.000 0.808
#> SRR1487890 1 0.0880 0.75726 0.968 0.032 0.000 0.000 0.000
#> SRR1349456 3 0.2674 0.44815 0.000 0.140 0.856 0.000 0.004
#> SRR1389384 3 0.6166 0.25133 0.000 0.272 0.548 0.000 0.180
#> SRR1316096 4 0.0324 0.85120 0.000 0.004 0.000 0.992 0.004
#> SRR1408512 5 0.3421 0.53091 0.080 0.000 0.080 0.000 0.840
#> SRR1447547 5 0.5996 -0.09895 0.000 0.352 0.124 0.000 0.524
#> SRR1354053 4 0.2519 0.84478 0.000 0.100 0.000 0.884 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.4385 0.6112 0.236 0.052 0.004 0.000 0.704 0.004
#> SRR1349562 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.4554 0.5874 0.000 0.024 0.000 0.568 0.008 0.400
#> SRR1499040 1 0.2101 0.8208 0.912 0.028 0.008 0.000 0.000 0.052
#> SRR1322312 1 0.1780 0.8266 0.924 0.028 0.000 0.000 0.000 0.048
#> SRR1324412 1 0.7405 0.2261 0.436 0.108 0.280 0.000 0.156 0.020
#> SRR1100991 3 0.7027 0.3490 0.152 0.108 0.528 0.000 0.192 0.020
#> SRR1349479 4 0.2163 0.7397 0.000 0.016 0.000 0.892 0.000 0.092
#> SRR1431248 5 0.3646 0.4383 0.000 0.012 0.068 0.000 0.808 0.112
#> SRR1405054 5 0.2663 0.5297 0.000 0.012 0.084 0.000 0.876 0.028
#> SRR1312266 1 0.3254 0.7648 0.816 0.048 0.000 0.000 0.136 0.000
#> SRR1409790 3 0.6135 0.4480 0.068 0.108 0.628 0.000 0.176 0.020
#> SRR1352507 3 0.4528 0.4414 0.000 0.016 0.632 0.000 0.328 0.024
#> SRR1383763 1 0.1780 0.8266 0.924 0.028 0.000 0.000 0.000 0.048
#> SRR1468314 4 0.0820 0.7672 0.000 0.012 0.000 0.972 0.000 0.016
#> SRR1473674 2 0.4267 0.5492 0.000 0.732 0.116 0.000 0.000 0.152
#> SRR1390499 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR821043 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
#> SRR1455653 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
#> SRR1335236 2 0.4212 0.5959 0.000 0.688 0.264 0.000 0.000 0.048
#> SRR1095383 4 0.0260 0.7708 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1479489 1 0.6726 0.4461 0.572 0.108 0.132 0.000 0.168 0.020
#> SRR1310433 4 0.0363 0.7713 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1073435 6 0.5503 0.6166 0.000 0.108 0.012 0.020 0.216 0.644
#> SRR659649 3 0.4275 0.4362 0.000 0.192 0.728 0.000 0.004 0.076
#> SRR1395999 1 0.4033 0.6903 0.760 0.080 0.000 0.000 0.156 0.004
#> SRR1105248 6 0.4103 0.6285 0.000 0.016 0.016 0.004 0.244 0.720
#> SRR1338257 5 0.5738 0.3532 0.376 0.080 0.020 0.000 0.516 0.008
#> SRR1499395 3 0.1867 0.5534 0.000 0.020 0.916 0.000 0.064 0.000
#> SRR1350002 2 0.3641 0.6249 0.000 0.748 0.224 0.000 0.000 0.028
#> SRR1489757 3 0.5808 0.4652 0.048 0.108 0.656 0.000 0.168 0.020
#> SRR1414637 5 0.3564 0.4584 0.000 0.076 0.016 0.000 0.820 0.088
#> SRR1478113 6 0.5086 -0.0536 0.000 0.032 0.000 0.272 0.056 0.640
#> SRR1322477 5 0.4404 0.3435 0.000 0.024 0.088 0.000 0.752 0.136
#> SRR1478789 3 0.3183 0.4900 0.000 0.128 0.828 0.000 0.004 0.040
#> SRR1414185 3 0.4551 0.4544 0.000 0.160 0.724 0.000 0.012 0.104
#> SRR1069141 2 0.4624 0.5137 0.000 0.688 0.120 0.000 0.000 0.192
#> SRR1376852 1 0.1657 0.8400 0.928 0.016 0.000 0.000 0.056 0.000
#> SRR1323491 5 0.4385 0.6112 0.236 0.052 0.004 0.000 0.704 0.004
#> SRR1338103 5 0.5243 0.5778 0.252 0.068 0.024 0.000 0.648 0.008
#> SRR1472012 5 0.6323 0.5090 0.268 0.092 0.068 0.000 0.560 0.012
#> SRR1340325 1 0.3393 0.7634 0.820 0.068 0.000 0.000 0.108 0.004
#> SRR1087321 3 0.3381 0.4733 0.000 0.156 0.800 0.000 0.000 0.044
#> SRR1488790 5 0.4410 0.6077 0.240 0.052 0.004 0.000 0.700 0.004
#> SRR1334866 3 0.6094 0.2579 0.000 0.064 0.516 0.000 0.336 0.084
#> SRR1089446 6 0.6891 0.5587 0.000 0.112 0.124 0.000 0.340 0.424
#> SRR1344445 3 0.6085 0.4512 0.064 0.108 0.632 0.000 0.176 0.020
#> SRR1412969 3 0.4341 0.4559 0.000 0.168 0.736 0.000 0.008 0.088
#> SRR1071668 3 0.5360 0.4596 0.000 0.084 0.688 0.000 0.116 0.112
#> SRR1075804 5 0.4352 0.5683 0.280 0.052 0.000 0.000 0.668 0.000
#> SRR1383283 4 0.4957 0.2953 0.000 0.072 0.000 0.544 0.000 0.384
#> SRR1350239 6 0.4677 0.6332 0.000 0.024 0.028 0.000 0.308 0.640
#> SRR1353878 1 0.2773 0.8083 0.868 0.064 0.000 0.000 0.064 0.004
#> SRR1375721 1 0.6492 -0.2028 0.420 0.092 0.064 0.000 0.416 0.008
#> SRR1083983 5 0.6336 0.5539 0.216 0.096 0.088 0.000 0.588 0.012
#> SRR1090095 1 0.1625 0.8413 0.928 0.012 0.000 0.000 0.060 0.000
#> SRR1414792 1 0.1563 0.8424 0.932 0.012 0.000 0.000 0.056 0.000
#> SRR1075102 4 0.5004 0.6477 0.000 0.052 0.000 0.612 0.020 0.316
#> SRR1098737 5 0.2308 0.6138 0.076 0.012 0.000 0.000 0.896 0.016
#> SRR1349409 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1413008 6 0.4677 0.6332 0.000 0.024 0.028 0.000 0.308 0.640
#> SRR1407179 3 0.6313 0.4366 0.076 0.108 0.608 0.000 0.188 0.020
#> SRR1095913 3 0.1995 0.5430 0.000 0.052 0.912 0.000 0.036 0.000
#> SRR1403544 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 5 0.1716 0.5919 0.036 0.004 0.000 0.000 0.932 0.028
#> SRR807971 3 0.4397 0.4509 0.000 0.012 0.648 0.000 0.316 0.024
#> SRR1436228 5 0.5894 0.0793 0.000 0.092 0.140 0.000 0.632 0.136
#> SRR1445218 4 0.3678 0.6668 0.000 0.084 0.000 0.788 0.000 0.128
#> SRR1485438 2 0.4571 0.6263 0.000 0.636 0.312 0.000 0.048 0.004
#> SRR1358143 1 0.1780 0.8266 0.924 0.028 0.000 0.000 0.000 0.048
#> SRR1328760 5 0.6603 0.4854 0.264 0.088 0.100 0.000 0.536 0.012
#> SRR1380806 1 0.0806 0.8471 0.972 0.020 0.000 0.000 0.000 0.008
#> SRR1379426 3 0.1682 0.5567 0.000 0.020 0.928 0.000 0.052 0.000
#> SRR1087007 3 0.4069 0.4754 0.000 0.148 0.764 0.000 0.008 0.080
#> SRR1086256 6 0.6882 0.5517 0.000 0.136 0.100 0.000 0.344 0.420
#> SRR1346734 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
#> SRR1414515 1 0.3285 0.7629 0.820 0.064 0.000 0.000 0.116 0.000
#> SRR1082151 2 0.4886 0.6181 0.000 0.612 0.312 0.000 0.072 0.004
#> SRR1349320 4 0.4709 0.5856 0.000 0.032 0.000 0.548 0.008 0.412
#> SRR1317554 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
#> SRR1076022 4 0.3686 0.6657 0.000 0.088 0.000 0.788 0.000 0.124
#> SRR1339573 3 0.6417 0.4159 0.136 0.112 0.612 0.000 0.120 0.020
#> SRR1455878 5 0.5428 0.5687 0.256 0.068 0.028 0.000 0.636 0.012
#> SRR1446203 3 0.4275 0.4362 0.000 0.192 0.728 0.000 0.004 0.076
#> SRR1387397 5 0.5977 0.6044 0.136 0.096 0.108 0.000 0.648 0.012
#> SRR1402590 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.3634 0.4424 0.000 0.012 0.064 0.000 0.808 0.116
#> SRR1331488 5 0.4508 0.0830 0.000 0.012 0.040 0.000 0.668 0.280
#> SRR1499675 5 0.2414 0.5494 0.000 0.012 0.056 0.000 0.896 0.036
#> SRR1440467 6 0.7496 0.2242 0.000 0.224 0.164 0.164 0.012 0.436
#> SRR807995 2 0.4612 0.6271 0.000 0.636 0.308 0.000 0.052 0.004
#> SRR1476485 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
#> SRR1388214 5 0.3308 0.4811 0.000 0.012 0.064 0.000 0.836 0.088
#> SRR1456051 1 0.3435 0.7495 0.804 0.060 0.000 0.000 0.136 0.000
#> SRR1473275 3 0.6758 0.3837 0.168 0.108 0.568 0.000 0.136 0.020
#> SRR1444083 5 0.6913 0.3557 0.320 0.092 0.112 0.000 0.464 0.012
#> SRR1313807 4 0.4949 0.3056 0.000 0.072 0.000 0.548 0.000 0.380
#> SRR1470751 2 0.5310 0.3575 0.000 0.544 0.088 0.000 0.360 0.008
#> SRR1403434 6 0.7404 0.2601 0.000 0.220 0.172 0.132 0.016 0.460
#> SRR1390540 5 0.1672 0.5863 0.016 0.004 0.012 0.000 0.940 0.028
#> SRR1093861 2 0.6122 0.1204 0.000 0.516 0.020 0.220 0.000 0.244
#> SRR1325290 5 0.6095 0.5673 0.220 0.088 0.072 0.000 0.608 0.012
#> SRR1070689 1 0.0632 0.8513 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1384049 1 0.1780 0.8266 0.924 0.028 0.000 0.000 0.000 0.048
#> SRR1081184 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8545 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 3 0.5679 0.3634 0.000 0.180 0.640 0.000 0.060 0.120
#> SRR1321877 3 0.3023 0.4940 0.000 0.120 0.836 0.000 0.000 0.044
#> SRR815711 3 0.5735 0.3889 0.000 0.068 0.632 0.000 0.196 0.104
#> SRR1433476 4 0.1838 0.7527 0.000 0.016 0.000 0.916 0.000 0.068
#> SRR1101883 3 0.4570 0.3949 0.000 0.012 0.596 0.000 0.368 0.024
#> SRR1433729 6 0.6423 0.4859 0.000 0.176 0.028 0.108 0.080 0.608
#> SRR1341877 5 0.1151 0.5960 0.000 0.012 0.032 0.000 0.956 0.000
#> SRR1090556 5 0.2860 0.5146 0.000 0.012 0.068 0.000 0.868 0.052
#> SRR1357389 3 0.1194 0.5588 0.000 0.004 0.956 0.000 0.032 0.008
#> SRR1404227 3 0.2988 0.5340 0.000 0.060 0.852 0.000 0.084 0.004
#> SRR1376830 1 0.1500 0.8430 0.936 0.012 0.000 0.000 0.052 0.000
#> SRR1500661 5 0.5427 0.5399 0.280 0.072 0.024 0.000 0.616 0.008
#> SRR1080294 4 0.0291 0.7709 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1336314 4 0.4658 0.6383 0.000 0.040 0.000 0.612 0.008 0.340
#> SRR1102152 3 0.7610 0.0336 0.196 0.112 0.400 0.000 0.272 0.020
#> SRR1345244 3 0.4163 0.4444 0.000 0.184 0.740 0.000 0.004 0.072
#> SRR1478637 3 0.7788 -0.0164 0.336 0.124 0.356 0.000 0.148 0.036
#> SRR1443776 3 0.2728 0.5079 0.000 0.100 0.860 0.000 0.000 0.040
#> SRR1120939 3 0.2365 0.5191 0.000 0.072 0.888 0.000 0.000 0.040
#> SRR1080117 3 0.1616 0.5571 0.000 0.020 0.932 0.000 0.048 0.000
#> SRR1102899 4 0.3686 0.6657 0.000 0.088 0.000 0.788 0.000 0.124
#> SRR1091865 5 0.6916 0.4600 0.252 0.108 0.120 0.000 0.508 0.012
#> SRR1361072 5 0.2534 0.5538 0.008 0.012 0.052 0.000 0.896 0.032
#> SRR1487890 1 0.0520 0.8514 0.984 0.008 0.000 0.000 0.000 0.008
#> SRR1349456 3 0.4275 0.4333 0.000 0.192 0.728 0.000 0.004 0.076
#> SRR1389384 2 0.5135 0.6029 0.000 0.592 0.308 0.000 0.096 0.004
#> SRR1316096 4 0.0363 0.7713 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1408512 5 0.0922 0.5940 0.004 0.004 0.024 0.000 0.968 0.000
#> SRR1447547 6 0.5245 0.6288 0.000 0.044 0.036 0.000 0.340 0.580
#> SRR1354053 4 0.3703 0.7570 0.000 0.060 0.000 0.800 0.012 0.128
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.990 0.995 0.4814 0.521 0.521
#> 3 3 0.913 0.911 0.961 0.3216 0.777 0.593
#> 4 4 0.771 0.810 0.896 0.1063 0.942 0.838
#> 5 5 0.843 0.849 0.902 0.0811 0.917 0.732
#> 6 6 0.853 0.825 0.895 0.0373 0.957 0.823
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.000 0.993 1.000 0.000
#> SRR1349562 1 0.000 0.993 1.000 0.000
#> SRR1353376 2 0.000 0.999 0.000 1.000
#> SRR1499040 1 0.000 0.993 1.000 0.000
#> SRR1322312 1 0.000 0.993 1.000 0.000
#> SRR1324412 1 0.000 0.993 1.000 0.000
#> SRR1100991 1 0.000 0.993 1.000 0.000
#> SRR1349479 2 0.000 0.999 0.000 1.000
#> SRR1431248 2 0.000 0.999 0.000 1.000
#> SRR1405054 1 0.000 0.993 1.000 0.000
#> SRR1312266 1 0.000 0.993 1.000 0.000
#> SRR1409790 1 0.000 0.993 1.000 0.000
#> SRR1352507 1 0.000 0.993 1.000 0.000
#> SRR1383763 1 0.000 0.993 1.000 0.000
#> SRR1468314 2 0.000 0.999 0.000 1.000
#> SRR1473674 2 0.000 0.999 0.000 1.000
#> SRR1390499 1 0.000 0.993 1.000 0.000
#> SRR821043 2 0.000 0.999 0.000 1.000
#> SRR1455653 2 0.000 0.999 0.000 1.000
#> SRR1335236 2 0.000 0.999 0.000 1.000
#> SRR1095383 2 0.000 0.999 0.000 1.000
#> SRR1479489 1 0.000 0.993 1.000 0.000
#> SRR1310433 2 0.000 0.999 0.000 1.000
#> SRR1073435 2 0.000 0.999 0.000 1.000
#> SRR659649 2 0.000 0.999 0.000 1.000
#> SRR1395999 1 0.000 0.993 1.000 0.000
#> SRR1105248 2 0.000 0.999 0.000 1.000
#> SRR1338257 1 0.000 0.993 1.000 0.000
#> SRR1499395 1 0.000 0.993 1.000 0.000
#> SRR1350002 2 0.000 0.999 0.000 1.000
#> SRR1489757 1 0.000 0.993 1.000 0.000
#> SRR1414637 1 0.000 0.993 1.000 0.000
#> SRR1478113 2 0.000 0.999 0.000 1.000
#> SRR1322477 2 0.000 0.999 0.000 1.000
#> SRR1478789 1 0.000 0.993 1.000 0.000
#> SRR1414185 2 0.000 0.999 0.000 1.000
#> SRR1069141 2 0.000 0.999 0.000 1.000
#> SRR1376852 1 0.000 0.993 1.000 0.000
#> SRR1323491 1 0.000 0.993 1.000 0.000
#> SRR1338103 1 0.000 0.993 1.000 0.000
#> SRR1472012 1 0.000 0.993 1.000 0.000
#> SRR1340325 1 0.000 0.993 1.000 0.000
#> SRR1087321 2 0.204 0.967 0.032 0.968
#> SRR1488790 1 0.000 0.993 1.000 0.000
#> SRR1334866 1 0.738 0.740 0.792 0.208
#> SRR1089446 2 0.000 0.999 0.000 1.000
#> SRR1344445 1 0.000 0.993 1.000 0.000
#> SRR1412969 2 0.000 0.999 0.000 1.000
#> SRR1071668 2 0.000 0.999 0.000 1.000
#> SRR1075804 1 0.000 0.993 1.000 0.000
#> SRR1383283 2 0.000 0.999 0.000 1.000
#> SRR1350239 2 0.000 0.999 0.000 1.000
#> SRR1353878 1 0.000 0.993 1.000 0.000
#> SRR1375721 1 0.000 0.993 1.000 0.000
#> SRR1083983 1 0.000 0.993 1.000 0.000
#> SRR1090095 1 0.000 0.993 1.000 0.000
#> SRR1414792 1 0.000 0.993 1.000 0.000
#> SRR1075102 2 0.000 0.999 0.000 1.000
#> SRR1098737 1 0.000 0.993 1.000 0.000
#> SRR1349409 1 0.000 0.993 1.000 0.000
#> SRR1413008 2 0.000 0.999 0.000 1.000
#> SRR1407179 1 0.000 0.993 1.000 0.000
#> SRR1095913 1 0.000 0.993 1.000 0.000
#> SRR1403544 1 0.000 0.993 1.000 0.000
#> SRR1490546 1 0.000 0.993 1.000 0.000
#> SRR807971 1 0.000 0.993 1.000 0.000
#> SRR1436228 2 0.000 0.999 0.000 1.000
#> SRR1445218 2 0.000 0.999 0.000 1.000
#> SRR1485438 1 0.000 0.993 1.000 0.000
#> SRR1358143 1 0.000 0.993 1.000 0.000
#> SRR1328760 1 0.000 0.993 1.000 0.000
#> SRR1380806 1 0.000 0.993 1.000 0.000
#> SRR1379426 1 0.000 0.993 1.000 0.000
#> SRR1087007 2 0.000 0.999 0.000 1.000
#> SRR1086256 2 0.000 0.999 0.000 1.000
#> SRR1346734 2 0.000 0.999 0.000 1.000
#> SRR1414515 1 0.000 0.993 1.000 0.000
#> SRR1082151 1 0.000 0.993 1.000 0.000
#> SRR1349320 2 0.000 0.999 0.000 1.000
#> SRR1317554 2 0.000 0.999 0.000 1.000
#> SRR1076022 2 0.000 0.999 0.000 1.000
#> SRR1339573 1 0.000 0.993 1.000 0.000
#> SRR1455878 1 0.000 0.993 1.000 0.000
#> SRR1446203 2 0.000 0.999 0.000 1.000
#> SRR1387397 1 0.000 0.993 1.000 0.000
#> SRR1402590 1 0.000 0.993 1.000 0.000
#> SRR1317532 1 0.795 0.688 0.760 0.240
#> SRR1331488 2 0.000 0.999 0.000 1.000
#> SRR1499675 1 0.000 0.993 1.000 0.000
#> SRR1440467 2 0.000 0.999 0.000 1.000
#> SRR807995 1 0.000 0.993 1.000 0.000
#> SRR1476485 2 0.000 0.999 0.000 1.000
#> SRR1388214 1 0.000 0.993 1.000 0.000
#> SRR1456051 1 0.000 0.993 1.000 0.000
#> SRR1473275 1 0.000 0.993 1.000 0.000
#> SRR1444083 1 0.000 0.993 1.000 0.000
#> SRR1313807 2 0.000 0.999 0.000 1.000
#> SRR1470751 1 0.000 0.993 1.000 0.000
#> SRR1403434 2 0.000 0.999 0.000 1.000
#> SRR1390540 1 0.000 0.993 1.000 0.000
#> SRR1093861 2 0.000 0.999 0.000 1.000
#> SRR1325290 1 0.000 0.993 1.000 0.000
#> SRR1070689 1 0.000 0.993 1.000 0.000
#> SRR1384049 1 0.000 0.993 1.000 0.000
#> SRR1081184 1 0.000 0.993 1.000 0.000
#> SRR1324295 1 0.000 0.993 1.000 0.000
#> SRR1365313 2 0.184 0.971 0.028 0.972
#> SRR1321877 1 0.204 0.962 0.968 0.032
#> SRR815711 2 0.000 0.999 0.000 1.000
#> SRR1433476 2 0.000 0.999 0.000 1.000
#> SRR1101883 1 0.000 0.993 1.000 0.000
#> SRR1433729 2 0.000 0.999 0.000 1.000
#> SRR1341877 1 0.000 0.993 1.000 0.000
#> SRR1090556 1 0.000 0.993 1.000 0.000
#> SRR1357389 1 0.000 0.993 1.000 0.000
#> SRR1404227 1 0.000 0.993 1.000 0.000
#> SRR1376830 1 0.000 0.993 1.000 0.000
#> SRR1500661 1 0.000 0.993 1.000 0.000
#> SRR1080294 2 0.000 0.999 0.000 1.000
#> SRR1336314 2 0.000 0.999 0.000 1.000
#> SRR1102152 1 0.000 0.993 1.000 0.000
#> SRR1345244 2 0.000 0.999 0.000 1.000
#> SRR1478637 1 0.000 0.993 1.000 0.000
#> SRR1443776 1 0.000 0.993 1.000 0.000
#> SRR1120939 1 0.494 0.878 0.892 0.108
#> SRR1080117 1 0.000 0.993 1.000 0.000
#> SRR1102899 2 0.000 0.999 0.000 1.000
#> SRR1091865 1 0.000 0.993 1.000 0.000
#> SRR1361072 1 0.000 0.993 1.000 0.000
#> SRR1487890 1 0.000 0.993 1.000 0.000
#> SRR1349456 2 0.000 0.999 0.000 1.000
#> SRR1389384 1 0.000 0.993 1.000 0.000
#> SRR1316096 2 0.000 0.999 0.000 1.000
#> SRR1408512 1 0.000 0.993 1.000 0.000
#> SRR1447547 2 0.000 0.999 0.000 1.000
#> SRR1354053 2 0.000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1499040 1 0.0592 0.963 0.988 0.000 0.012
#> SRR1322312 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1324412 1 0.0592 0.963 0.988 0.000 0.012
#> SRR1100991 1 0.4555 0.696 0.800 0.000 0.200
#> SRR1349479 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1431248 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1405054 1 0.0237 0.971 0.996 0.000 0.004
#> SRR1312266 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1409790 3 0.6079 0.509 0.388 0.000 0.612
#> SRR1352507 3 0.6079 0.509 0.388 0.000 0.612
#> SRR1383763 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.975 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1335236 3 0.1163 0.842 0.000 0.028 0.972
#> SRR1095383 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1073435 2 0.0000 0.996 0.000 1.000 0.000
#> SRR659649 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1105248 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1338257 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1350002 2 0.4002 0.816 0.000 0.840 0.160
#> SRR1489757 3 0.6079 0.509 0.388 0.000 0.612
#> SRR1414637 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1478113 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1322477 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1478789 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1340325 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1334866 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1089446 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1344445 3 0.6095 0.501 0.392 0.000 0.608
#> SRR1412969 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1383283 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1350239 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1353878 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1413008 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1407179 1 0.6154 0.127 0.592 0.000 0.408
#> SRR1095913 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1403544 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.975 1.000 0.000 0.000
#> SRR807971 3 0.3879 0.777 0.152 0.000 0.848
#> SRR1436228 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1445218 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1485438 3 0.6095 0.473 0.392 0.000 0.608
#> SRR1358143 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1380806 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1086256 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1346734 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1082151 1 0.1753 0.926 0.952 0.000 0.048
#> SRR1349320 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1339573 3 0.6045 0.522 0.380 0.000 0.620
#> SRR1455878 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1387397 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1402590 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1317532 1 0.5733 0.501 0.676 0.324 0.000
#> SRR1331488 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1499675 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1440467 2 0.0000 0.996 0.000 1.000 0.000
#> SRR807995 3 0.6192 0.414 0.420 0.000 0.580
#> SRR1476485 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1388214 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1456051 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1473275 3 0.6244 0.387 0.440 0.000 0.560
#> SRR1444083 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1313807 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1470751 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1403434 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1365313 3 0.1289 0.839 0.000 0.032 0.968
#> SRR1321877 3 0.0000 0.862 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1433476 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1101883 3 0.6079 0.509 0.388 0.000 0.612
#> SRR1433729 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1341877 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1090556 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1357389 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1404227 3 0.2066 0.836 0.060 0.000 0.940
#> SRR1376830 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1102152 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1345244 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1478637 1 0.4555 0.696 0.800 0.000 0.200
#> SRR1443776 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1361072 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1487890 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1389384 1 0.2165 0.907 0.936 0.000 0.064
#> SRR1316096 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.975 1.000 0.000 0.000
#> SRR1447547 2 0.0000 0.996 0.000 1.000 0.000
#> SRR1354053 2 0.0000 0.996 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.4134 0.725 0.740 0.260 0.000 0.000
#> SRR1349562 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1499040 1 0.0779 0.870 0.980 0.004 0.016 0.000
#> SRR1322312 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1324412 1 0.4456 0.525 0.716 0.004 0.280 0.000
#> SRR1100991 1 0.4584 0.487 0.696 0.004 0.300 0.000
#> SRR1349479 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1431248 4 0.4343 0.624 0.004 0.264 0.000 0.732
#> SRR1405054 1 0.6164 0.630 0.644 0.264 0.092 0.000
#> SRR1312266 1 0.1389 0.863 0.952 0.048 0.000 0.000
#> SRR1409790 3 0.3908 0.682 0.212 0.004 0.784 0.000
#> SRR1352507 3 0.4059 0.692 0.200 0.012 0.788 0.000
#> SRR1383763 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1468314 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1473674 2 0.4679 0.420 0.000 0.648 0.000 0.352
#> SRR1390499 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR821043 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.4737 0.511 0.000 0.728 0.252 0.020
#> SRR1095383 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1479489 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1310433 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1073435 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR659649 3 0.2408 0.824 0.000 0.104 0.896 0.000
#> SRR1395999 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1105248 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1338257 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1499395 3 0.0895 0.817 0.020 0.004 0.976 0.000
#> SRR1350002 2 0.5395 0.601 0.000 0.732 0.084 0.184
#> SRR1489757 3 0.3870 0.687 0.208 0.004 0.788 0.000
#> SRR1414637 2 0.3400 0.616 0.180 0.820 0.000 0.000
#> SRR1478113 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1322477 4 0.2081 0.874 0.000 0.084 0.000 0.916
#> SRR1478789 3 0.2704 0.814 0.000 0.124 0.876 0.000
#> SRR1414185 3 0.2408 0.824 0.000 0.104 0.896 0.000
#> SRR1069141 4 0.4948 0.142 0.000 0.440 0.000 0.560
#> SRR1376852 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1323491 1 0.3907 0.749 0.768 0.232 0.000 0.000
#> SRR1338103 1 0.1867 0.851 0.928 0.072 0.000 0.000
#> SRR1472012 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1340325 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1087321 3 0.2589 0.818 0.000 0.116 0.884 0.000
#> SRR1488790 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1334866 2 0.5232 0.491 0.012 0.644 0.340 0.004
#> SRR1089446 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1344445 3 0.3945 0.677 0.216 0.004 0.780 0.000
#> SRR1412969 3 0.2408 0.824 0.000 0.104 0.896 0.000
#> SRR1071668 3 0.1389 0.825 0.000 0.048 0.952 0.000
#> SRR1075804 1 0.3219 0.796 0.836 0.164 0.000 0.000
#> SRR1383283 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1350239 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1353878 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1375721 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1083983 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1090095 1 0.2281 0.840 0.904 0.096 0.000 0.000
#> SRR1414792 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1075102 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1349409 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1413008 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1407179 1 0.4819 0.382 0.652 0.004 0.344 0.000
#> SRR1095913 3 0.3117 0.792 0.092 0.028 0.880 0.000
#> SRR1403544 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR807971 3 0.2300 0.801 0.064 0.016 0.920 0.000
#> SRR1436228 2 0.4454 0.479 0.000 0.692 0.000 0.308
#> SRR1445218 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1485438 2 0.4632 0.677 0.308 0.688 0.004 0.000
#> SRR1358143 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1328760 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1380806 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1379426 3 0.1824 0.806 0.060 0.004 0.936 0.000
#> SRR1087007 3 0.2345 0.825 0.000 0.100 0.900 0.000
#> SRR1086256 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1346734 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.4522 0.674 0.320 0.680 0.000 0.000
#> SRR1349320 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1076022 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1339573 3 0.3908 0.682 0.212 0.004 0.784 0.000
#> SRR1455878 1 0.1474 0.863 0.948 0.052 0.000 0.000
#> SRR1446203 3 0.2530 0.820 0.000 0.112 0.888 0.000
#> SRR1387397 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1402590 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.6273 0.582 0.636 0.264 0.000 0.100
#> SRR1331488 4 0.2921 0.808 0.000 0.140 0.000 0.860
#> SRR1499675 1 0.4134 0.725 0.740 0.260 0.000 0.000
#> SRR1440467 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR807995 2 0.4655 0.677 0.312 0.684 0.004 0.000
#> SRR1476485 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1456051 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1473275 3 0.4978 0.371 0.384 0.004 0.612 0.000
#> SRR1444083 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1313807 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1470751 2 0.4624 0.653 0.340 0.660 0.000 0.000
#> SRR1403434 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1390540 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1093861 4 0.4643 0.418 0.000 0.344 0.000 0.656
#> SRR1325290 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1070689 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1384049 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1081184 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.882 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.5184 0.553 0.000 0.732 0.212 0.056
#> SRR1321877 3 0.2589 0.818 0.000 0.116 0.884 0.000
#> SRR815711 3 0.1118 0.824 0.000 0.036 0.964 0.000
#> SRR1433476 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1101883 3 0.4175 0.690 0.200 0.016 0.784 0.000
#> SRR1433729 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1341877 1 0.4134 0.725 0.740 0.260 0.000 0.000
#> SRR1090556 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1357389 3 0.0000 0.818 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.3249 0.751 0.140 0.008 0.852 0.000
#> SRR1376830 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1500661 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> SRR1080294 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1336314 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.1824 0.835 0.936 0.004 0.060 0.000
#> SRR1345244 3 0.2589 0.818 0.000 0.116 0.884 0.000
#> SRR1478637 1 0.3306 0.708 0.840 0.004 0.156 0.000
#> SRR1443776 3 0.2408 0.824 0.000 0.104 0.896 0.000
#> SRR1120939 3 0.2216 0.825 0.000 0.092 0.908 0.000
#> SRR1080117 3 0.0376 0.817 0.004 0.004 0.992 0.000
#> SRR1102899 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1091865 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1361072 1 0.4164 0.721 0.736 0.264 0.000 0.000
#> SRR1487890 1 0.0188 0.881 0.996 0.004 0.000 0.000
#> SRR1349456 3 0.2647 0.815 0.000 0.120 0.880 0.000
#> SRR1389384 2 0.4522 0.674 0.320 0.680 0.000 0.000
#> SRR1316096 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1408512 1 0.3726 0.761 0.788 0.212 0.000 0.000
#> SRR1447547 4 0.0000 0.960 0.000 0.000 0.000 1.000
#> SRR1354053 4 0.0000 0.960 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 5 0.3266 0.729 0.200 0.004 0.000 0.000 0.796
#> SRR1349562 1 0.0324 0.960 0.992 0.004 0.000 0.000 0.004
#> SRR1353376 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1499040 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1322312 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1324412 1 0.4201 0.662 0.752 0.000 0.204 0.000 0.044
#> SRR1100991 1 0.4201 0.662 0.752 0.000 0.204 0.000 0.044
#> SRR1349479 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1431248 5 0.1484 0.756 0.000 0.008 0.000 0.048 0.944
#> SRR1405054 5 0.0693 0.768 0.012 0.000 0.008 0.000 0.980
#> SRR1312266 1 0.0955 0.944 0.968 0.004 0.000 0.000 0.028
#> SRR1409790 3 0.3995 0.673 0.180 0.000 0.776 0.000 0.044
#> SRR1352507 3 0.3844 0.690 0.164 0.000 0.792 0.000 0.044
#> SRR1383763 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1468314 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1473674 2 0.3366 0.640 0.000 0.768 0.000 0.232 0.000
#> SRR1390499 1 0.0451 0.959 0.988 0.004 0.000 0.000 0.008
#> SRR821043 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1455653 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1335236 2 0.1831 0.731 0.000 0.920 0.076 0.000 0.004
#> SRR1095383 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1310433 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1073435 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR659649 3 0.3209 0.793 0.000 0.180 0.812 0.000 0.008
#> SRR1395999 1 0.0162 0.960 0.996 0.004 0.000 0.000 0.000
#> SRR1105248 4 0.0162 0.972 0.000 0.000 0.000 0.996 0.004
#> SRR1338257 1 0.0162 0.960 0.996 0.004 0.000 0.000 0.000
#> SRR1499395 3 0.1205 0.783 0.004 0.000 0.956 0.000 0.040
#> SRR1350002 2 0.0290 0.771 0.000 0.992 0.008 0.000 0.000
#> SRR1489757 3 0.3844 0.690 0.164 0.000 0.792 0.000 0.044
#> SRR1414637 2 0.3488 0.790 0.168 0.808 0.000 0.000 0.024
#> SRR1478113 4 0.0290 0.970 0.000 0.000 0.000 0.992 0.008
#> SRR1322477 5 0.4435 0.427 0.000 0.016 0.000 0.336 0.648
#> SRR1478789 3 0.3455 0.777 0.000 0.208 0.784 0.000 0.008
#> SRR1414185 3 0.3171 0.794 0.000 0.176 0.816 0.000 0.008
#> SRR1069141 4 0.4227 0.258 0.000 0.420 0.000 0.580 0.000
#> SRR1376852 1 0.0451 0.959 0.988 0.004 0.000 0.000 0.008
#> SRR1323491 5 0.3689 0.673 0.256 0.004 0.000 0.000 0.740
#> SRR1338103 1 0.0865 0.948 0.972 0.004 0.000 0.000 0.024
#> SRR1472012 1 0.0290 0.960 0.992 0.000 0.000 0.000 0.008
#> SRR1340325 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.3318 0.788 0.000 0.192 0.800 0.000 0.008
#> SRR1488790 5 0.1768 0.816 0.072 0.004 0.000 0.000 0.924
#> SRR1334866 2 0.2964 0.741 0.004 0.840 0.152 0.000 0.004
#> SRR1089446 4 0.0324 0.968 0.000 0.004 0.000 0.992 0.004
#> SRR1344445 3 0.3995 0.673 0.180 0.000 0.776 0.000 0.044
#> SRR1412969 3 0.3282 0.790 0.000 0.188 0.804 0.000 0.008
#> SRR1071668 3 0.2660 0.798 0.000 0.128 0.864 0.000 0.008
#> SRR1075804 1 0.3884 0.523 0.708 0.004 0.000 0.000 0.288
#> SRR1383283 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1350239 4 0.0290 0.970 0.000 0.000 0.000 0.992 0.008
#> SRR1353878 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1375721 1 0.0162 0.961 0.996 0.000 0.000 0.000 0.004
#> SRR1083983 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1090095 1 0.3430 0.669 0.776 0.004 0.000 0.000 0.220
#> SRR1414792 1 0.0671 0.954 0.980 0.004 0.000 0.000 0.016
#> SRR1075102 4 0.0162 0.972 0.000 0.000 0.000 0.996 0.004
#> SRR1098737 5 0.1892 0.812 0.080 0.004 0.000 0.000 0.916
#> SRR1349409 1 0.0451 0.959 0.988 0.004 0.000 0.000 0.008
#> SRR1413008 4 0.0290 0.970 0.000 0.000 0.000 0.992 0.008
#> SRR1407179 1 0.1907 0.893 0.928 0.000 0.044 0.000 0.028
#> SRR1095913 3 0.2675 0.777 0.020 0.040 0.900 0.000 0.040
#> SRR1403544 1 0.0290 0.960 0.992 0.000 0.000 0.000 0.008
#> SRR1490546 5 0.1571 0.817 0.060 0.004 0.000 0.000 0.936
#> SRR807971 3 0.1408 0.782 0.008 0.000 0.948 0.000 0.044
#> SRR1436228 2 0.3970 0.723 0.000 0.800 0.000 0.104 0.096
#> SRR1445218 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1485438 2 0.2471 0.820 0.136 0.864 0.000 0.000 0.000
#> SRR1358143 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1328760 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1380806 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1379426 3 0.1331 0.783 0.008 0.000 0.952 0.000 0.040
#> SRR1087007 3 0.3171 0.794 0.000 0.176 0.816 0.000 0.008
#> SRR1086256 4 0.0324 0.968 0.000 0.004 0.000 0.992 0.004
#> SRR1346734 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1414515 1 0.0290 0.960 0.992 0.000 0.000 0.000 0.008
#> SRR1082151 2 0.2891 0.803 0.176 0.824 0.000 0.000 0.000
#> SRR1349320 4 0.0162 0.972 0.000 0.000 0.000 0.996 0.004
#> SRR1317554 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1076022 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1339573 3 0.3922 0.675 0.180 0.000 0.780 0.000 0.040
#> SRR1455878 1 0.0865 0.948 0.972 0.004 0.000 0.000 0.024
#> SRR1446203 3 0.3318 0.788 0.000 0.192 0.800 0.000 0.008
#> SRR1387397 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1402590 1 0.0451 0.959 0.988 0.004 0.000 0.000 0.008
#> SRR1317532 5 0.1522 0.806 0.044 0.000 0.000 0.012 0.944
#> SRR1331488 5 0.2813 0.654 0.000 0.000 0.000 0.168 0.832
#> SRR1499675 5 0.3814 0.650 0.276 0.004 0.000 0.000 0.720
#> SRR1440467 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR807995 2 0.2561 0.819 0.144 0.856 0.000 0.000 0.000
#> SRR1476485 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1388214 5 0.1410 0.817 0.060 0.000 0.000 0.000 0.940
#> SRR1456051 1 0.0566 0.957 0.984 0.004 0.000 0.000 0.012
#> SRR1473275 3 0.5230 0.135 0.452 0.000 0.504 0.000 0.044
#> SRR1444083 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1313807 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1470751 2 0.2929 0.793 0.180 0.820 0.000 0.000 0.000
#> SRR1403434 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1390540 5 0.1410 0.817 0.060 0.000 0.000 0.000 0.940
#> SRR1093861 4 0.4030 0.431 0.000 0.352 0.000 0.648 0.000
#> SRR1325290 1 0.0290 0.960 0.992 0.000 0.000 0.000 0.008
#> SRR1070689 1 0.0566 0.957 0.984 0.004 0.000 0.000 0.012
#> SRR1384049 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1081184 1 0.0162 0.960 0.996 0.004 0.000 0.000 0.000
#> SRR1324295 1 0.0162 0.960 0.996 0.004 0.000 0.000 0.000
#> SRR1365313 2 0.1764 0.739 0.000 0.928 0.064 0.000 0.008
#> SRR1321877 3 0.3318 0.788 0.000 0.192 0.800 0.000 0.008
#> SRR815711 3 0.2411 0.798 0.000 0.108 0.884 0.000 0.008
#> SRR1433476 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1101883 3 0.3882 0.686 0.168 0.000 0.788 0.000 0.044
#> SRR1433729 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1341877 5 0.3579 0.694 0.240 0.004 0.000 0.000 0.756
#> SRR1090556 5 0.1410 0.817 0.060 0.000 0.000 0.000 0.940
#> SRR1357389 3 0.1121 0.783 0.000 0.000 0.956 0.000 0.044
#> SRR1404227 3 0.2798 0.760 0.060 0.008 0.888 0.000 0.044
#> SRR1376830 1 0.0451 0.959 0.988 0.004 0.000 0.000 0.008
#> SRR1500661 1 0.0566 0.957 0.984 0.004 0.000 0.000 0.012
#> SRR1080294 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1336314 4 0.0162 0.972 0.000 0.000 0.000 0.996 0.004
#> SRR1102152 1 0.1907 0.890 0.928 0.000 0.028 0.000 0.044
#> SRR1345244 3 0.3318 0.788 0.000 0.192 0.800 0.000 0.008
#> SRR1478637 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1443776 3 0.3246 0.792 0.000 0.184 0.808 0.000 0.008
#> SRR1120939 3 0.3171 0.795 0.000 0.176 0.816 0.000 0.008
#> SRR1080117 3 0.1205 0.783 0.004 0.000 0.956 0.000 0.040
#> SRR1102899 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1091865 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1361072 5 0.1410 0.817 0.060 0.000 0.000 0.000 0.940
#> SRR1487890 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> SRR1349456 3 0.3388 0.783 0.000 0.200 0.792 0.000 0.008
#> SRR1389384 2 0.2852 0.806 0.172 0.828 0.000 0.000 0.000
#> SRR1316096 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
#> SRR1408512 5 0.4452 0.160 0.496 0.004 0.000 0.000 0.500
#> SRR1447547 4 0.0290 0.970 0.000 0.000 0.000 0.992 0.008
#> SRR1354053 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 5 0.3601 0.6197 0.312 0.004 0.000 0.000 0.684 0.000
#> SRR1349562 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1353376 4 0.1845 0.9009 0.000 0.008 0.072 0.916 0.004 0.000
#> SRR1499040 1 0.0363 0.9471 0.988 0.000 0.012 0.000 0.000 0.000
#> SRR1322312 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1324412 3 0.2762 0.6916 0.196 0.000 0.804 0.000 0.000 0.000
#> SRR1100991 3 0.2762 0.6916 0.196 0.000 0.804 0.000 0.000 0.000
#> SRR1349479 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1431248 5 0.0603 0.7234 0.000 0.000 0.004 0.016 0.980 0.000
#> SRR1405054 5 0.3279 0.6499 0.028 0.000 0.176 0.000 0.796 0.000
#> SRR1312266 1 0.0777 0.9335 0.972 0.004 0.000 0.000 0.024 0.000
#> SRR1409790 3 0.3394 0.8493 0.052 0.000 0.804 0.000 0.000 0.144
#> SRR1352507 3 0.3315 0.8503 0.040 0.000 0.804 0.000 0.000 0.156
#> SRR1383763 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1468314 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1473674 2 0.3650 0.5787 0.000 0.716 0.004 0.272 0.000 0.008
#> SRR1390499 1 0.0146 0.9523 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR821043 4 0.1701 0.9016 0.000 0.008 0.072 0.920 0.000 0.000
#> SRR1455653 4 0.1845 0.9009 0.000 0.008 0.072 0.916 0.004 0.000
#> SRR1335236 2 0.4304 0.1968 0.000 0.536 0.008 0.008 0.000 0.448
#> SRR1095383 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1479489 1 0.0260 0.9507 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1310433 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1073435 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR659649 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1105248 4 0.3322 0.8678 0.000 0.012 0.104 0.832 0.052 0.000
#> SRR1338257 1 0.0291 0.9503 0.992 0.004 0.000 0.000 0.004 0.000
#> SRR1499395 3 0.3531 0.7625 0.000 0.000 0.672 0.000 0.000 0.328
#> SRR1350002 2 0.1141 0.8370 0.000 0.948 0.000 0.000 0.000 0.052
#> SRR1489757 3 0.3315 0.8503 0.040 0.000 0.804 0.000 0.000 0.156
#> SRR1414637 2 0.0748 0.8468 0.004 0.976 0.004 0.000 0.016 0.000
#> SRR1478113 4 0.3322 0.8678 0.000 0.012 0.104 0.832 0.052 0.000
#> SRR1322477 5 0.5292 0.3772 0.000 0.016 0.108 0.252 0.624 0.000
#> SRR1478789 6 0.0622 0.8855 0.000 0.012 0.008 0.000 0.000 0.980
#> SRR1414185 6 0.0260 0.8915 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1069141 4 0.4587 0.3424 0.000 0.372 0.004 0.588 0.000 0.036
#> SRR1376852 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1323491 5 0.3841 0.5058 0.380 0.004 0.000 0.000 0.616 0.000
#> SRR1338103 1 0.1219 0.9085 0.948 0.004 0.000 0.000 0.048 0.000
#> SRR1472012 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1340325 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1087321 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1488790 5 0.1806 0.7752 0.088 0.004 0.000 0.000 0.908 0.000
#> SRR1334866 2 0.2753 0.8068 0.000 0.872 0.092 0.004 0.016 0.016
#> SRR1089446 4 0.2742 0.8281 0.000 0.036 0.072 0.876 0.016 0.000
#> SRR1344445 3 0.3394 0.8493 0.052 0.000 0.804 0.000 0.000 0.144
#> SRR1412969 6 0.0146 0.8939 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1071668 6 0.3126 0.5899 0.000 0.000 0.248 0.000 0.000 0.752
#> SRR1075804 1 0.3601 0.4556 0.684 0.004 0.000 0.000 0.312 0.000
#> SRR1383283 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1350239 4 0.3383 0.8653 0.000 0.012 0.104 0.828 0.056 0.000
#> SRR1353878 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1090095 1 0.3189 0.6283 0.760 0.004 0.000 0.000 0.236 0.000
#> SRR1414792 1 0.0405 0.9476 0.988 0.004 0.000 0.000 0.008 0.000
#> SRR1075102 4 0.3322 0.8678 0.000 0.012 0.104 0.832 0.052 0.000
#> SRR1098737 5 0.2149 0.7689 0.104 0.004 0.004 0.000 0.888 0.000
#> SRR1349409 1 0.0146 0.9523 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR1413008 4 0.3383 0.8653 0.000 0.012 0.104 0.828 0.056 0.000
#> SRR1407179 1 0.2793 0.7221 0.800 0.000 0.200 0.000 0.000 0.000
#> SRR1095913 3 0.4090 0.7422 0.004 0.016 0.652 0.000 0.000 0.328
#> SRR1403544 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1490546 5 0.1471 0.7771 0.064 0.004 0.000 0.000 0.932 0.000
#> SRR807971 3 0.2762 0.8283 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR1436228 2 0.4116 0.7422 0.000 0.780 0.088 0.108 0.024 0.000
#> SRR1445218 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1485438 2 0.1297 0.8613 0.040 0.948 0.000 0.000 0.000 0.012
#> SRR1358143 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1328760 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1380806 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1379426 3 0.3547 0.7585 0.000 0.000 0.668 0.000 0.000 0.332
#> SRR1087007 6 0.0260 0.8915 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1086256 4 0.2380 0.8497 0.000 0.036 0.048 0.900 0.016 0.000
#> SRR1346734 4 0.1901 0.8998 0.000 0.008 0.076 0.912 0.004 0.000
#> SRR1414515 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082151 2 0.1265 0.8598 0.044 0.948 0.000 0.000 0.000 0.008
#> SRR1349320 4 0.3127 0.8739 0.000 0.012 0.104 0.844 0.040 0.000
#> SRR1317554 4 0.1701 0.9016 0.000 0.008 0.072 0.920 0.000 0.000
#> SRR1076022 4 0.0146 0.9060 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1339573 3 0.4007 0.8339 0.052 0.000 0.728 0.000 0.000 0.220
#> SRR1455878 1 0.1700 0.8730 0.916 0.004 0.000 0.000 0.080 0.000
#> SRR1446203 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1387397 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317532 5 0.0653 0.7279 0.004 0.000 0.004 0.012 0.980 0.000
#> SRR1331488 5 0.3710 0.5953 0.000 0.012 0.108 0.076 0.804 0.000
#> SRR1499675 5 0.4118 0.4646 0.396 0.004 0.008 0.000 0.592 0.000
#> SRR1440467 4 0.2730 0.7793 0.000 0.000 0.012 0.836 0.000 0.152
#> SRR807995 2 0.1297 0.8613 0.040 0.948 0.000 0.000 0.000 0.012
#> SRR1476485 4 0.1845 0.9009 0.000 0.008 0.072 0.916 0.004 0.000
#> SRR1388214 5 0.1349 0.7712 0.056 0.004 0.000 0.000 0.940 0.000
#> SRR1456051 1 0.0146 0.9523 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1473275 3 0.3558 0.7946 0.112 0.000 0.800 0.000 0.000 0.088
#> SRR1444083 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1313807 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1470751 2 0.1196 0.8598 0.040 0.952 0.000 0.000 0.000 0.008
#> SRR1403434 4 0.2531 0.8015 0.000 0.000 0.012 0.856 0.000 0.132
#> SRR1390540 5 0.1444 0.7780 0.072 0.000 0.000 0.000 0.928 0.000
#> SRR1093861 4 0.3411 0.6415 0.000 0.232 0.008 0.756 0.000 0.004
#> SRR1325290 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1070689 1 0.0291 0.9503 0.992 0.004 0.000 0.000 0.004 0.000
#> SRR1384049 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1365313 6 0.6763 0.0104 0.000 0.348 0.088 0.084 0.016 0.464
#> SRR1321877 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815711 6 0.5766 0.3271 0.000 0.036 0.392 0.044 0.016 0.512
#> SRR1433476 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1101883 3 0.3394 0.8493 0.052 0.000 0.804 0.000 0.000 0.144
#> SRR1433729 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1341877 5 0.3976 0.5031 0.380 0.004 0.004 0.000 0.612 0.000
#> SRR1090556 5 0.1701 0.7777 0.072 0.000 0.008 0.000 0.920 0.000
#> SRR1357389 3 0.2793 0.8262 0.000 0.000 0.800 0.000 0.000 0.200
#> SRR1404227 3 0.3724 0.7658 0.012 0.004 0.716 0.000 0.000 0.268
#> SRR1376830 1 0.0146 0.9523 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9538 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1080294 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1336314 4 0.3322 0.8678 0.000 0.012 0.104 0.832 0.052 0.000
#> SRR1102152 1 0.2340 0.7864 0.852 0.000 0.148 0.000 0.000 0.000
#> SRR1345244 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1478637 1 0.0363 0.9471 0.988 0.000 0.012 0.000 0.000 0.000
#> SRR1443776 6 0.0000 0.8953 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1120939 6 0.0260 0.8916 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1080117 3 0.3547 0.7585 0.000 0.000 0.668 0.000 0.000 0.332
#> SRR1102899 4 0.0146 0.9060 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1091865 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1361072 5 0.1327 0.7766 0.064 0.000 0.000 0.000 0.936 0.000
#> SRR1487890 1 0.0146 0.9533 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1349456 6 0.0458 0.8859 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1389384 2 0.1265 0.8598 0.044 0.948 0.000 0.000 0.000 0.008
#> SRR1316096 4 0.0000 0.9075 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1408512 1 0.3930 0.1032 0.576 0.004 0.000 0.000 0.420 0.000
#> SRR1447547 4 0.3322 0.8678 0.000 0.012 0.104 0.832 0.052 0.000
#> SRR1354053 4 0.1845 0.9009 0.000 0.008 0.072 0.916 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.990 0.3414 0.662 0.662
#> 3 3 0.658 0.833 0.867 0.8184 0.646 0.490
#> 4 4 0.668 0.787 0.884 0.1618 0.877 0.680
#> 5 5 0.681 0.798 0.858 0.0368 0.972 0.902
#> 6 6 0.771 0.779 0.863 0.0397 0.960 0.853
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.992 1.000 0.000
#> SRR1349562 1 0.0000 0.992 1.000 0.000
#> SRR1353376 2 0.0000 0.981 0.000 1.000
#> SRR1499040 1 0.0000 0.992 1.000 0.000
#> SRR1322312 1 0.0000 0.992 1.000 0.000
#> SRR1324412 1 0.0000 0.992 1.000 0.000
#> SRR1100991 1 0.0000 0.992 1.000 0.000
#> SRR1349479 2 0.0000 0.981 0.000 1.000
#> SRR1431248 1 0.0000 0.992 1.000 0.000
#> SRR1405054 1 0.0000 0.992 1.000 0.000
#> SRR1312266 1 0.0000 0.992 1.000 0.000
#> SRR1409790 1 0.0000 0.992 1.000 0.000
#> SRR1352507 1 0.0000 0.992 1.000 0.000
#> SRR1383763 1 0.0000 0.992 1.000 0.000
#> SRR1468314 2 0.0000 0.981 0.000 1.000
#> SRR1473674 1 0.9909 0.178 0.556 0.444
#> SRR1390499 1 0.0000 0.992 1.000 0.000
#> SRR821043 2 0.0000 0.981 0.000 1.000
#> SRR1455653 2 0.0000 0.981 0.000 1.000
#> SRR1335236 1 0.0000 0.992 1.000 0.000
#> SRR1095383 2 0.0000 0.981 0.000 1.000
#> SRR1479489 1 0.0000 0.992 1.000 0.000
#> SRR1310433 2 0.0000 0.981 0.000 1.000
#> SRR1073435 2 0.4690 0.879 0.100 0.900
#> SRR659649 1 0.0000 0.992 1.000 0.000
#> SRR1395999 1 0.0000 0.992 1.000 0.000
#> SRR1105248 1 0.9850 0.230 0.572 0.428
#> SRR1338257 1 0.0000 0.992 1.000 0.000
#> SRR1499395 1 0.0000 0.992 1.000 0.000
#> SRR1350002 1 0.0000 0.992 1.000 0.000
#> SRR1489757 1 0.0000 0.992 1.000 0.000
#> SRR1414637 1 0.0000 0.992 1.000 0.000
#> SRR1478113 2 0.0938 0.971 0.012 0.988
#> SRR1322477 1 0.0000 0.992 1.000 0.000
#> SRR1478789 1 0.0000 0.992 1.000 0.000
#> SRR1414185 1 0.0000 0.992 1.000 0.000
#> SRR1069141 2 0.9775 0.292 0.412 0.588
#> SRR1376852 1 0.0000 0.992 1.000 0.000
#> SRR1323491 1 0.0000 0.992 1.000 0.000
#> SRR1338103 1 0.0000 0.992 1.000 0.000
#> SRR1472012 1 0.0000 0.992 1.000 0.000
#> SRR1340325 1 0.0000 0.992 1.000 0.000
#> SRR1087321 1 0.0000 0.992 1.000 0.000
#> SRR1488790 1 0.0000 0.992 1.000 0.000
#> SRR1334866 1 0.0000 0.992 1.000 0.000
#> SRR1089446 1 0.0000 0.992 1.000 0.000
#> SRR1344445 1 0.0000 0.992 1.000 0.000
#> SRR1412969 1 0.0000 0.992 1.000 0.000
#> SRR1071668 1 0.0000 0.992 1.000 0.000
#> SRR1075804 1 0.0000 0.992 1.000 0.000
#> SRR1383283 2 0.0000 0.981 0.000 1.000
#> SRR1350239 1 0.0000 0.992 1.000 0.000
#> SRR1353878 1 0.0000 0.992 1.000 0.000
#> SRR1375721 1 0.0000 0.992 1.000 0.000
#> SRR1083983 1 0.0000 0.992 1.000 0.000
#> SRR1090095 1 0.0000 0.992 1.000 0.000
#> SRR1414792 1 0.0000 0.992 1.000 0.000
#> SRR1075102 2 0.0000 0.981 0.000 1.000
#> SRR1098737 1 0.0000 0.992 1.000 0.000
#> SRR1349409 1 0.0000 0.992 1.000 0.000
#> SRR1413008 1 0.0000 0.992 1.000 0.000
#> SRR1407179 1 0.0000 0.992 1.000 0.000
#> SRR1095913 1 0.0000 0.992 1.000 0.000
#> SRR1403544 1 0.0000 0.992 1.000 0.000
#> SRR1490546 1 0.0000 0.992 1.000 0.000
#> SRR807971 1 0.0000 0.992 1.000 0.000
#> SRR1436228 1 0.0000 0.992 1.000 0.000
#> SRR1445218 2 0.0000 0.981 0.000 1.000
#> SRR1485438 1 0.0000 0.992 1.000 0.000
#> SRR1358143 1 0.0000 0.992 1.000 0.000
#> SRR1328760 1 0.0000 0.992 1.000 0.000
#> SRR1380806 1 0.0000 0.992 1.000 0.000
#> SRR1379426 1 0.0000 0.992 1.000 0.000
#> SRR1087007 1 0.0000 0.992 1.000 0.000
#> SRR1086256 1 0.0000 0.992 1.000 0.000
#> SRR1346734 2 0.0000 0.981 0.000 1.000
#> SRR1414515 1 0.0000 0.992 1.000 0.000
#> SRR1082151 1 0.0000 0.992 1.000 0.000
#> SRR1349320 2 0.0000 0.981 0.000 1.000
#> SRR1317554 2 0.0000 0.981 0.000 1.000
#> SRR1076022 2 0.0000 0.981 0.000 1.000
#> SRR1339573 1 0.0000 0.992 1.000 0.000
#> SRR1455878 1 0.0000 0.992 1.000 0.000
#> SRR1446203 1 0.0000 0.992 1.000 0.000
#> SRR1387397 1 0.0000 0.992 1.000 0.000
#> SRR1402590 1 0.0000 0.992 1.000 0.000
#> SRR1317532 1 0.0000 0.992 1.000 0.000
#> SRR1331488 1 0.0000 0.992 1.000 0.000
#> SRR1499675 1 0.0000 0.992 1.000 0.000
#> SRR1440467 2 0.0000 0.981 0.000 1.000
#> SRR807995 1 0.0000 0.992 1.000 0.000
#> SRR1476485 2 0.0000 0.981 0.000 1.000
#> SRR1388214 1 0.0000 0.992 1.000 0.000
#> SRR1456051 1 0.0000 0.992 1.000 0.000
#> SRR1473275 1 0.0000 0.992 1.000 0.000
#> SRR1444083 1 0.0000 0.992 1.000 0.000
#> SRR1313807 2 0.0000 0.981 0.000 1.000
#> SRR1470751 1 0.0000 0.992 1.000 0.000
#> SRR1403434 2 0.0000 0.981 0.000 1.000
#> SRR1390540 1 0.0000 0.992 1.000 0.000
#> SRR1093861 2 0.0000 0.981 0.000 1.000
#> SRR1325290 1 0.0000 0.992 1.000 0.000
#> SRR1070689 1 0.0000 0.992 1.000 0.000
#> SRR1384049 1 0.0000 0.992 1.000 0.000
#> SRR1081184 1 0.0000 0.992 1.000 0.000
#> SRR1324295 1 0.0000 0.992 1.000 0.000
#> SRR1365313 1 0.0000 0.992 1.000 0.000
#> SRR1321877 1 0.0000 0.992 1.000 0.000
#> SRR815711 1 0.0000 0.992 1.000 0.000
#> SRR1433476 2 0.0000 0.981 0.000 1.000
#> SRR1101883 1 0.0000 0.992 1.000 0.000
#> SRR1433729 2 0.0000 0.981 0.000 1.000
#> SRR1341877 1 0.0000 0.992 1.000 0.000
#> SRR1090556 1 0.0000 0.992 1.000 0.000
#> SRR1357389 1 0.0000 0.992 1.000 0.000
#> SRR1404227 1 0.0000 0.992 1.000 0.000
#> SRR1376830 1 0.0000 0.992 1.000 0.000
#> SRR1500661 1 0.0000 0.992 1.000 0.000
#> SRR1080294 2 0.0000 0.981 0.000 1.000
#> SRR1336314 2 0.0000 0.981 0.000 1.000
#> SRR1102152 1 0.0000 0.992 1.000 0.000
#> SRR1345244 1 0.0000 0.992 1.000 0.000
#> SRR1478637 1 0.0000 0.992 1.000 0.000
#> SRR1443776 1 0.0000 0.992 1.000 0.000
#> SRR1120939 1 0.0000 0.992 1.000 0.000
#> SRR1080117 1 0.0000 0.992 1.000 0.000
#> SRR1102899 2 0.0000 0.981 0.000 1.000
#> SRR1091865 1 0.0000 0.992 1.000 0.000
#> SRR1361072 1 0.0000 0.992 1.000 0.000
#> SRR1487890 1 0.0000 0.992 1.000 0.000
#> SRR1349456 1 0.0000 0.992 1.000 0.000
#> SRR1389384 1 0.0000 0.992 1.000 0.000
#> SRR1316096 2 0.0000 0.981 0.000 1.000
#> SRR1408512 1 0.0000 0.992 1.000 0.000
#> SRR1447547 1 0.0000 0.992 1.000 0.000
#> SRR1354053 2 0.0000 0.981 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.3038 0.886 0.896 0.000 0.104
#> SRR1349562 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1499040 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1322312 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1324412 3 0.3879 0.791 0.152 0.000 0.848
#> SRR1100991 3 0.3752 0.795 0.144 0.000 0.856
#> SRR1349479 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1431248 3 0.6062 0.456 0.384 0.000 0.616
#> SRR1405054 3 0.3686 0.799 0.140 0.000 0.860
#> SRR1312266 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1409790 3 0.1753 0.855 0.048 0.000 0.952
#> SRR1352507 3 0.0592 0.867 0.012 0.000 0.988
#> SRR1383763 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1473674 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1390499 1 0.0000 0.932 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1335236 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1095383 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1479489 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1310433 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1073435 3 0.0000 0.870 0.000 0.000 1.000
#> SRR659649 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1105248 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1338257 1 0.3752 0.843 0.856 0.000 0.144
#> SRR1499395 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1350002 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1489757 3 0.0237 0.869 0.004 0.000 0.996
#> SRR1414637 3 0.6062 0.456 0.384 0.000 0.616
#> SRR1478113 3 0.6305 0.144 0.000 0.484 0.516
#> SRR1322477 3 0.3340 0.815 0.120 0.000 0.880
#> SRR1478789 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1414185 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1069141 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1376852 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1323491 1 0.3340 0.871 0.880 0.000 0.120
#> SRR1338103 1 0.3116 0.883 0.892 0.000 0.108
#> SRR1472012 1 0.0424 0.930 0.992 0.000 0.008
#> SRR1340325 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1488790 1 0.2959 0.888 0.900 0.000 0.100
#> SRR1334866 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1089446 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1344445 3 0.2711 0.835 0.088 0.000 0.912
#> SRR1412969 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1075804 1 0.2796 0.893 0.908 0.000 0.092
#> SRR1383283 2 0.1643 0.932 0.000 0.956 0.044
#> SRR1350239 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1353878 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1083983 1 0.3116 0.883 0.892 0.000 0.108
#> SRR1090095 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1098737 3 0.6252 0.311 0.444 0.000 0.556
#> SRR1349409 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1413008 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1407179 1 0.3116 0.883 0.892 0.000 0.108
#> SRR1095913 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1403544 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1490546 3 0.6299 0.206 0.476 0.000 0.524
#> SRR807971 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1436228 3 0.3116 0.823 0.108 0.000 0.892
#> SRR1445218 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1485438 1 0.3551 0.860 0.868 0.000 0.132
#> SRR1358143 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1328760 1 0.3038 0.886 0.896 0.000 0.104
#> SRR1380806 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1379426 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1086256 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1346734 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1082151 1 0.4654 0.756 0.792 0.000 0.208
#> SRR1349320 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1339573 3 0.6154 0.309 0.408 0.000 0.592
#> SRR1455878 1 0.2448 0.902 0.924 0.000 0.076
#> SRR1446203 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1387397 3 0.6154 0.403 0.408 0.000 0.592
#> SRR1402590 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1317532 3 0.5138 0.680 0.252 0.000 0.748
#> SRR1331488 3 0.1529 0.858 0.040 0.000 0.960
#> SRR1499675 3 0.5810 0.546 0.336 0.000 0.664
#> SRR1440467 2 0.6140 0.423 0.000 0.596 0.404
#> SRR807995 1 0.1163 0.924 0.972 0.000 0.028
#> SRR1476485 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1388214 3 0.2959 0.828 0.100 0.000 0.900
#> SRR1456051 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1473275 1 0.3752 0.792 0.856 0.000 0.144
#> SRR1444083 3 0.6252 0.311 0.444 0.000 0.556
#> SRR1313807 2 0.3752 0.841 0.000 0.856 0.144
#> SRR1470751 1 0.5216 0.653 0.740 0.000 0.260
#> SRR1403434 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1390540 3 0.6154 0.403 0.408 0.000 0.592
#> SRR1093861 2 0.5254 0.687 0.000 0.736 0.264
#> SRR1325290 1 0.3941 0.828 0.844 0.000 0.156
#> SRR1070689 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1365313 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1321877 3 0.0000 0.870 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1433476 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1101883 3 0.1289 0.860 0.032 0.000 0.968
#> SRR1433729 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1341877 3 0.6260 0.299 0.448 0.000 0.552
#> SRR1090556 3 0.5178 0.675 0.256 0.000 0.744
#> SRR1357389 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1500661 1 0.3038 0.886 0.896 0.000 0.104
#> SRR1080294 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1102152 3 0.3752 0.795 0.144 0.000 0.856
#> SRR1345244 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1478637 1 0.0237 0.931 0.996 0.000 0.004
#> SRR1443776 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1091865 1 0.3116 0.883 0.892 0.000 0.108
#> SRR1361072 3 0.5178 0.675 0.256 0.000 0.744
#> SRR1487890 1 0.0000 0.932 1.000 0.000 0.000
#> SRR1349456 3 0.0000 0.870 0.000 0.000 1.000
#> SRR1389384 1 0.6062 0.337 0.616 0.000 0.384
#> SRR1316096 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1408512 3 0.6154 0.403 0.408 0.000 0.592
#> SRR1447547 3 0.1163 0.862 0.028 0.000 0.972
#> SRR1354053 2 0.0000 0.963 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.4830 0.5585 0.608 0.000 0.392 0.000
#> SRR1349562 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.0336 0.9576 0.000 0.008 0.000 0.992
#> SRR1499040 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1322312 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.5092 0.7685 0.096 0.140 0.764 0.000
#> SRR1100991 3 0.4906 0.7760 0.084 0.140 0.776 0.000
#> SRR1349479 4 0.0336 0.9576 0.000 0.008 0.000 0.992
#> SRR1431248 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1405054 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1312266 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1409790 3 0.4387 0.7805 0.024 0.200 0.776 0.000
#> SRR1352507 3 0.3837 0.7743 0.000 0.224 0.776 0.000
#> SRR1383763 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1468314 4 0.0592 0.9554 0.000 0.016 0.000 0.984
#> SRR1473674 2 0.0000 0.8924 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1095383 4 0.0336 0.9576 0.000 0.008 0.000 0.992
#> SRR1479489 1 0.2675 0.7864 0.892 0.100 0.008 0.000
#> SRR1310433 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1073435 3 0.1022 0.8278 0.000 0.032 0.968 0.000
#> SRR659649 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1395999 1 0.0336 0.8374 0.992 0.000 0.008 0.000
#> SRR1105248 3 0.1022 0.8278 0.000 0.032 0.968 0.000
#> SRR1338257 1 0.3400 0.7632 0.820 0.000 0.180 0.000
#> SRR1499395 2 0.4356 0.4360 0.000 0.708 0.292 0.000
#> SRR1350002 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1489757 3 0.3837 0.7743 0.000 0.224 0.776 0.000
#> SRR1414637 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1478113 4 0.5253 0.4360 0.000 0.016 0.360 0.624
#> SRR1322477 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1478789 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1414185 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1069141 2 0.0000 0.8924 0.000 1.000 0.000 0.000
#> SRR1376852 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.3764 0.7366 0.784 0.000 0.216 0.000
#> SRR1338103 1 0.4697 0.5966 0.644 0.000 0.356 0.000
#> SRR1472012 1 0.3569 0.7647 0.804 0.000 0.196 0.000
#> SRR1340325 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1087321 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1488790 1 0.4193 0.7093 0.732 0.000 0.268 0.000
#> SRR1334866 3 0.4605 0.6588 0.000 0.336 0.664 0.000
#> SRR1089446 3 0.4989 0.1943 0.000 0.472 0.528 0.000
#> SRR1344445 3 0.4852 0.7791 0.072 0.152 0.776 0.000
#> SRR1412969 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1071668 3 0.3942 0.7677 0.000 0.236 0.764 0.000
#> SRR1075804 1 0.4134 0.7181 0.740 0.000 0.260 0.000
#> SRR1383283 4 0.2813 0.8856 0.000 0.024 0.080 0.896
#> SRR1350239 3 0.0592 0.8355 0.000 0.016 0.984 0.000
#> SRR1353878 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0336 0.8374 0.992 0.000 0.008 0.000
#> SRR1083983 1 0.4697 0.5966 0.644 0.000 0.356 0.000
#> SRR1090095 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1414792 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1098737 3 0.0336 0.8352 0.008 0.000 0.992 0.000
#> SRR1349409 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1413008 3 0.0592 0.8355 0.000 0.016 0.984 0.000
#> SRR1407179 1 0.6377 0.5931 0.632 0.112 0.256 0.000
#> SRR1095913 3 0.3907 0.7704 0.000 0.232 0.768 0.000
#> SRR1403544 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1490546 3 0.2589 0.7466 0.116 0.000 0.884 0.000
#> SRR807971 3 0.3907 0.7704 0.000 0.232 0.768 0.000
#> SRR1436228 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1445218 4 0.0707 0.9538 0.000 0.020 0.000 0.980
#> SRR1485438 2 0.5452 0.0180 0.428 0.556 0.016 0.000
#> SRR1358143 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.3764 0.7424 0.784 0.000 0.216 0.000
#> SRR1380806 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1379426 3 0.4679 0.6350 0.000 0.352 0.648 0.000
#> SRR1087007 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1086256 3 0.3764 0.7440 0.000 0.216 0.784 0.000
#> SRR1346734 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0336 0.8374 0.992 0.000 0.008 0.000
#> SRR1082151 1 0.6724 0.5929 0.612 0.224 0.164 0.000
#> SRR1349320 4 0.0336 0.9576 0.000 0.008 0.000 0.992
#> SRR1317554 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1076022 4 0.0817 0.9518 0.000 0.024 0.000 0.976
#> SRR1339573 1 0.7674 -0.0167 0.428 0.220 0.352 0.000
#> SRR1455878 1 0.4522 0.6494 0.680 0.000 0.320 0.000
#> SRR1446203 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1387397 3 0.2760 0.7765 0.128 0.000 0.872 0.000
#> SRR1402590 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1317532 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1331488 3 0.0336 0.8354 0.000 0.008 0.992 0.000
#> SRR1499675 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1440467 2 0.2760 0.7538 0.000 0.872 0.000 0.128
#> SRR807995 1 0.5147 0.7006 0.740 0.200 0.060 0.000
#> SRR1476485 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1388214 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1456051 1 0.0336 0.8374 0.992 0.000 0.008 0.000
#> SRR1473275 1 0.4869 0.7281 0.780 0.132 0.088 0.000
#> SRR1444083 3 0.3907 0.6893 0.232 0.000 0.768 0.000
#> SRR1313807 4 0.3873 0.6903 0.000 0.228 0.000 0.772
#> SRR1470751 1 0.4972 0.3803 0.544 0.000 0.456 0.000
#> SRR1403434 2 0.0000 0.8924 0.000 1.000 0.000 0.000
#> SRR1390540 3 0.1211 0.8190 0.040 0.000 0.960 0.000
#> SRR1093861 2 0.4431 0.4843 0.000 0.696 0.000 0.304
#> SRR1325290 1 0.4761 0.5691 0.628 0.000 0.372 0.000
#> SRR1070689 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1081184 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.4040 0.6252 0.000 0.752 0.248 0.000
#> SRR1321877 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR815711 3 0.3907 0.7704 0.000 0.232 0.768 0.000
#> SRR1433476 4 0.0592 0.9554 0.000 0.016 0.000 0.984
#> SRR1101883 3 0.2921 0.8148 0.000 0.140 0.860 0.000
#> SRR1433729 3 0.4134 0.7226 0.000 0.260 0.740 0.000
#> SRR1341877 3 0.1211 0.8190 0.040 0.000 0.960 0.000
#> SRR1090556 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1357389 3 0.3907 0.7704 0.000 0.232 0.768 0.000
#> SRR1404227 3 0.4277 0.7280 0.000 0.280 0.720 0.000
#> SRR1376830 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.4222 0.7050 0.728 0.000 0.272 0.000
#> SRR1080294 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1336314 4 0.1022 0.9359 0.000 0.000 0.032 0.968
#> SRR1102152 3 0.4906 0.7760 0.084 0.140 0.776 0.000
#> SRR1345244 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1478637 1 0.4010 0.7683 0.836 0.100 0.064 0.000
#> SRR1443776 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1120939 3 0.4907 0.4965 0.000 0.420 0.580 0.000
#> SRR1080117 3 0.4661 0.6410 0.000 0.348 0.652 0.000
#> SRR1102899 4 0.0817 0.9518 0.000 0.024 0.000 0.976
#> SRR1091865 1 0.6221 0.6083 0.644 0.100 0.256 0.000
#> SRR1361072 3 0.0000 0.8380 0.000 0.000 1.000 0.000
#> SRR1487890 1 0.0000 0.8385 1.000 0.000 0.000 0.000
#> SRR1349456 2 0.0817 0.9063 0.000 0.976 0.024 0.000
#> SRR1389384 1 0.7747 0.2583 0.432 0.316 0.252 0.000
#> SRR1316096 4 0.0000 0.9582 0.000 0.000 0.000 1.000
#> SRR1408512 3 0.0592 0.8327 0.016 0.000 0.984 0.000
#> SRR1447547 3 0.0592 0.8355 0.000 0.016 0.984 0.000
#> SRR1354053 4 0.0000 0.9582 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.3318 0.8169 0.800 0.000 0.000 0.008 0.192
#> SRR1349562 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1353376 2 0.1043 0.9153 0.000 0.960 0.000 0.040 0.000
#> SRR1499040 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1322312 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1324412 5 0.2308 0.8155 0.048 0.000 0.036 0.004 0.912
#> SRR1100991 5 0.3011 0.8377 0.016 0.000 0.140 0.000 0.844
#> SRR1349479 2 0.1043 0.9153 0.000 0.960 0.000 0.040 0.000
#> SRR1431248 5 0.0000 0.8281 0.000 0.000 0.000 0.000 1.000
#> SRR1405054 5 0.2074 0.8392 0.000 0.000 0.104 0.000 0.896
#> SRR1312266 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1409790 5 0.2674 0.8370 0.004 0.000 0.140 0.000 0.856
#> SRR1352507 5 0.2561 0.8358 0.000 0.000 0.144 0.000 0.856
#> SRR1383763 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1468314 2 0.0703 0.9176 0.000 0.976 0.000 0.024 0.000
#> SRR1473674 3 0.0703 0.8653 0.000 0.024 0.976 0.000 0.000
#> SRR1390499 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR821043 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1455653 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1335236 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1095383 2 0.1043 0.9153 0.000 0.960 0.000 0.040 0.000
#> SRR1479489 1 0.3183 0.8342 0.856 0.000 0.028 0.008 0.108
#> SRR1310433 2 0.2424 0.8042 0.000 0.868 0.000 0.132 0.000
#> SRR1073435 5 0.5355 0.6089 0.000 0.220 0.000 0.120 0.660
#> SRR659649 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1395999 1 0.2411 0.8383 0.884 0.000 0.000 0.008 0.108
#> SRR1105248 5 0.5355 0.6089 0.000 0.220 0.000 0.120 0.660
#> SRR1338257 1 0.2707 0.8370 0.860 0.000 0.000 0.008 0.132
#> SRR1499395 3 0.3661 0.5107 0.000 0.000 0.724 0.000 0.276
#> SRR1350002 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1489757 5 0.2561 0.8358 0.000 0.000 0.144 0.000 0.856
#> SRR1414637 5 0.0000 0.8281 0.000 0.000 0.000 0.000 1.000
#> SRR1478113 4 0.1997 0.7520 0.000 0.040 0.000 0.924 0.036
#> SRR1322477 5 0.1695 0.8076 0.044 0.000 0.008 0.008 0.940
#> SRR1478789 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1414185 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1069141 3 0.2450 0.7859 0.000 0.028 0.896 0.076 0.000
#> SRR1376852 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1323491 1 0.3421 0.8068 0.788 0.000 0.000 0.008 0.204
#> SRR1338103 1 0.3885 0.7613 0.724 0.000 0.000 0.008 0.268
#> SRR1472012 1 0.3809 0.7718 0.736 0.000 0.000 0.008 0.256
#> SRR1340325 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1087321 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1488790 1 0.2929 0.8313 0.840 0.000 0.000 0.008 0.152
#> SRR1334866 5 0.3109 0.8033 0.000 0.000 0.200 0.000 0.800
#> SRR1089446 5 0.4862 0.5023 0.000 0.032 0.364 0.000 0.604
#> SRR1344445 5 0.2674 0.8370 0.004 0.000 0.140 0.000 0.856
#> SRR1412969 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1071668 5 0.2648 0.8327 0.000 0.000 0.152 0.000 0.848
#> SRR1075804 1 0.2886 0.8323 0.844 0.000 0.000 0.008 0.148
#> SRR1383283 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> SRR1350239 5 0.2964 0.8030 0.000 0.024 0.000 0.120 0.856
#> SRR1353878 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1375721 1 0.2411 0.8383 0.884 0.000 0.000 0.008 0.108
#> SRR1083983 1 0.3885 0.7613 0.724 0.000 0.000 0.008 0.268
#> SRR1090095 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1414792 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1075102 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1098737 5 0.1408 0.8017 0.044 0.000 0.000 0.008 0.948
#> SRR1349409 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1413008 5 0.2964 0.8030 0.000 0.024 0.000 0.120 0.856
#> SRR1407179 1 0.4598 0.7567 0.716 0.000 0.036 0.008 0.240
#> SRR1095913 5 0.2605 0.8341 0.000 0.000 0.148 0.000 0.852
#> SRR1403544 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1490546 5 0.4147 0.3414 0.316 0.000 0.000 0.008 0.676
#> SRR807971 5 0.2605 0.8341 0.000 0.000 0.148 0.000 0.852
#> SRR1436228 5 0.2074 0.8392 0.000 0.000 0.104 0.000 0.896
#> SRR1445218 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> SRR1485438 3 0.6177 -0.0486 0.388 0.000 0.496 0.008 0.108
#> SRR1358143 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1328760 1 0.3132 0.8243 0.820 0.000 0.000 0.008 0.172
#> SRR1380806 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1379426 5 0.3242 0.7901 0.000 0.000 0.216 0.000 0.784
#> SRR1087007 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1086256 5 0.3326 0.8129 0.000 0.024 0.152 0.000 0.824
#> SRR1346734 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1414515 1 0.2411 0.8383 0.884 0.000 0.000 0.008 0.108
#> SRR1082151 1 0.5683 0.6854 0.652 0.000 0.200 0.008 0.140
#> SRR1349320 4 0.4219 0.4574 0.000 0.416 0.000 0.584 0.000
#> SRR1317554 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1076022 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> SRR1339573 5 0.5223 0.4196 0.316 0.000 0.048 0.008 0.628
#> SRR1455878 1 0.3885 0.7613 0.724 0.000 0.000 0.008 0.268
#> SRR1446203 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1387397 5 0.1082 0.8164 0.028 0.000 0.000 0.008 0.964
#> SRR1402590 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1317532 5 0.0000 0.8281 0.000 0.000 0.000 0.000 1.000
#> SRR1331488 5 0.2873 0.8044 0.000 0.020 0.000 0.120 0.860
#> SRR1499675 5 0.0000 0.8281 0.000 0.000 0.000 0.000 1.000
#> SRR1440467 3 0.3796 0.5225 0.000 0.300 0.700 0.000 0.000
#> SRR807995 1 0.5503 0.7068 0.672 0.000 0.192 0.008 0.128
#> SRR1476485 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
#> SRR1388214 5 0.2074 0.8392 0.000 0.000 0.104 0.000 0.896
#> SRR1456051 1 0.1908 0.8420 0.908 0.000 0.000 0.000 0.092
#> SRR1473275 1 0.4425 0.7778 0.740 0.000 0.036 0.008 0.216
#> SRR1444083 5 0.2077 0.7967 0.084 0.000 0.000 0.008 0.908
#> SRR1313807 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> SRR1470751 1 0.4367 0.6041 0.620 0.000 0.000 0.008 0.372
#> SRR1403434 3 0.3274 0.6697 0.000 0.220 0.780 0.000 0.000
#> SRR1390540 5 0.3209 0.6581 0.180 0.000 0.000 0.008 0.812
#> SRR1093861 2 0.3913 0.4455 0.000 0.676 0.324 0.000 0.000
#> SRR1325290 1 0.3980 0.7440 0.708 0.000 0.000 0.008 0.284
#> SRR1070689 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1384049 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1081184 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1324295 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1365313 3 0.3143 0.6657 0.000 0.000 0.796 0.000 0.204
#> SRR1321877 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR815711 5 0.2605 0.8341 0.000 0.000 0.148 0.000 0.852
#> SRR1433476 2 0.0703 0.9176 0.000 0.976 0.000 0.024 0.000
#> SRR1101883 5 0.2329 0.8390 0.000 0.000 0.124 0.000 0.876
#> SRR1433729 5 0.6715 0.5556 0.000 0.220 0.068 0.120 0.592
#> SRR1341877 5 0.3093 0.6779 0.168 0.000 0.000 0.008 0.824
#> SRR1090556 5 0.0000 0.8281 0.000 0.000 0.000 0.000 1.000
#> SRR1357389 5 0.2605 0.8341 0.000 0.000 0.148 0.000 0.852
#> SRR1404227 5 0.2966 0.8151 0.000 0.000 0.184 0.000 0.816
#> SRR1376830 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1500661 1 0.3013 0.8289 0.832 0.000 0.000 0.008 0.160
#> SRR1080294 2 0.1197 0.9096 0.000 0.952 0.000 0.048 0.000
#> SRR1336314 4 0.1270 0.8106 0.000 0.052 0.000 0.948 0.000
#> SRR1102152 5 0.3902 0.8299 0.048 0.000 0.136 0.008 0.808
#> SRR1345244 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1478637 1 0.3852 0.8136 0.796 0.000 0.028 0.008 0.168
#> SRR1443776 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1120939 5 0.3913 0.6580 0.000 0.000 0.324 0.000 0.676
#> SRR1080117 5 0.3210 0.7934 0.000 0.000 0.212 0.000 0.788
#> SRR1102899 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> SRR1091865 1 0.4443 0.7632 0.724 0.000 0.028 0.008 0.240
#> SRR1361072 5 0.0404 0.8320 0.000 0.000 0.012 0.000 0.988
#> SRR1487890 1 0.1121 0.8466 0.956 0.000 0.000 0.044 0.000
#> SRR1349456 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> SRR1389384 1 0.6599 0.4515 0.508 0.000 0.284 0.008 0.200
#> SRR1316096 2 0.2020 0.8523 0.000 0.900 0.000 0.100 0.000
#> SRR1408512 5 0.1894 0.7823 0.072 0.000 0.000 0.008 0.920
#> SRR1447547 5 0.2964 0.8030 0.000 0.024 0.000 0.120 0.856
#> SRR1354053 4 0.2852 0.9119 0.000 0.172 0.000 0.828 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.0777 0.784 0.972 0.000 0.024 0.000 0.004 0.000
#> SRR1349562 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1353376 2 0.1267 0.891 0.000 0.940 0.000 0.060 0.000 0.000
#> SRR1499040 1 0.3528 0.785 0.700 0.000 0.004 0.000 0.296 0.000
#> SRR1322312 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1324412 3 0.2531 0.770 0.128 0.000 0.860 0.000 0.004 0.008
#> SRR1100991 3 0.1410 0.804 0.044 0.000 0.944 0.000 0.004 0.008
#> SRR1349479 2 0.1267 0.891 0.000 0.940 0.000 0.060 0.000 0.000
#> SRR1431248 3 0.1958 0.775 0.100 0.000 0.896 0.000 0.004 0.000
#> SRR1405054 3 0.0146 0.808 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1312266 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1409790 3 0.0405 0.809 0.000 0.000 0.988 0.000 0.004 0.008
#> SRR1352507 3 0.0692 0.806 0.000 0.000 0.976 0.000 0.004 0.020
#> SRR1383763 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1468314 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1473674 6 0.0291 0.954 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR1390499 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR821043 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1455653 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1335236 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1095383 2 0.1387 0.887 0.000 0.932 0.000 0.068 0.000 0.000
#> SRR1479489 1 0.0603 0.788 0.980 0.000 0.016 0.000 0.000 0.004
#> SRR1310433 2 0.2003 0.845 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR1073435 3 0.4833 0.596 0.000 0.132 0.728 0.088 0.052 0.000
#> SRR659649 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1395999 1 0.1913 0.799 0.908 0.000 0.012 0.000 0.080 0.000
#> SRR1105248 5 0.5358 0.792 0.000 0.100 0.100 0.088 0.704 0.008
#> SRR1338257 1 0.2250 0.798 0.888 0.000 0.020 0.000 0.092 0.000
#> SRR1499395 6 0.3126 0.617 0.000 0.000 0.248 0.000 0.000 0.752
#> SRR1350002 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1489757 3 0.0508 0.809 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1414637 3 0.0405 0.807 0.008 0.000 0.988 0.000 0.004 0.000
#> SRR1478113 5 0.4022 0.623 0.000 0.020 0.000 0.272 0.700 0.008
#> SRR1322477 3 0.3508 0.632 0.292 0.000 0.704 0.000 0.004 0.000
#> SRR1478789 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1414185 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1069141 6 0.0260 0.950 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1376852 1 0.3189 0.792 0.760 0.000 0.004 0.000 0.236 0.000
#> SRR1323491 1 0.0713 0.784 0.972 0.000 0.028 0.000 0.000 0.000
#> SRR1338103 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1472012 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1340325 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1087321 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1488790 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1334866 3 0.1556 0.779 0.000 0.000 0.920 0.000 0.000 0.080
#> SRR1089446 3 0.3911 0.350 0.000 0.008 0.624 0.000 0.000 0.368
#> SRR1344445 3 0.0508 0.809 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1412969 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1071668 3 0.0790 0.804 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1075804 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1383283 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1350239 5 0.4585 0.855 0.000 0.000 0.200 0.088 0.704 0.008
#> SRR1353878 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1375721 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1083983 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1090095 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1414792 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1075102 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1098737 3 0.3528 0.629 0.296 0.000 0.700 0.000 0.004 0.000
#> SRR1349409 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1413008 5 0.4585 0.855 0.000 0.000 0.200 0.088 0.704 0.008
#> SRR1407179 1 0.2070 0.759 0.892 0.000 0.100 0.000 0.000 0.008
#> SRR1095913 3 0.0713 0.805 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1403544 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1490546 1 0.3966 -0.162 0.552 0.000 0.444 0.000 0.004 0.000
#> SRR807971 3 0.0692 0.806 0.000 0.000 0.976 0.000 0.004 0.020
#> SRR1436228 3 0.0291 0.809 0.004 0.000 0.992 0.000 0.004 0.000
#> SRR1445218 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1485438 1 0.4165 0.178 0.536 0.000 0.012 0.000 0.000 0.452
#> SRR1358143 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1328760 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1380806 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1379426 3 0.1863 0.764 0.000 0.000 0.896 0.000 0.000 0.104
#> SRR1087007 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1086256 3 0.1858 0.768 0.000 0.004 0.904 0.000 0.000 0.092
#> SRR1346734 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1414515 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1082151 1 0.2164 0.766 0.900 0.000 0.032 0.000 0.000 0.068
#> SRR1349320 4 0.3860 0.135 0.000 0.472 0.000 0.528 0.000 0.000
#> SRR1317554 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1076022 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339573 3 0.4893 0.313 0.440 0.000 0.512 0.000 0.012 0.036
#> SRR1455878 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1446203 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1387397 3 0.2178 0.759 0.132 0.000 0.868 0.000 0.000 0.000
#> SRR1402590 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1317532 3 0.2053 0.771 0.108 0.000 0.888 0.000 0.004 0.000
#> SRR1331488 5 0.5358 0.760 0.104 0.000 0.096 0.088 0.704 0.008
#> SRR1499675 3 0.2520 0.747 0.152 0.000 0.844 0.000 0.004 0.000
#> SRR1440467 6 0.2178 0.839 0.000 0.132 0.000 0.000 0.000 0.868
#> SRR807995 1 0.1913 0.771 0.908 0.000 0.012 0.000 0.000 0.080
#> SRR1476485 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1388214 3 0.0146 0.808 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1456051 1 0.2260 0.800 0.860 0.000 0.000 0.000 0.140 0.000
#> SRR1473275 1 0.2213 0.756 0.888 0.000 0.100 0.000 0.004 0.008
#> SRR1444083 3 0.3489 0.636 0.288 0.000 0.708 0.000 0.004 0.000
#> SRR1313807 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1470751 1 0.2048 0.692 0.880 0.000 0.120 0.000 0.000 0.000
#> SRR1403434 6 0.2178 0.839 0.000 0.132 0.000 0.000 0.000 0.868
#> SRR1390540 3 0.3915 0.465 0.412 0.000 0.584 0.000 0.004 0.000
#> SRR1093861 2 0.3695 0.353 0.000 0.624 0.000 0.000 0.000 0.376
#> SRR1325290 1 0.0865 0.778 0.964 0.000 0.036 0.000 0.000 0.000
#> SRR1070689 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1384049 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1081184 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1324295 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1365313 6 0.2221 0.859 0.032 0.000 0.072 0.000 0.000 0.896
#> SRR1321877 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR815711 3 0.0713 0.805 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1433476 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1101883 3 0.0508 0.809 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1433729 3 0.7264 0.270 0.000 0.132 0.548 0.088 0.148 0.084
#> SRR1341877 3 0.3890 0.485 0.400 0.000 0.596 0.000 0.004 0.000
#> SRR1090556 3 0.0405 0.807 0.008 0.000 0.988 0.000 0.004 0.000
#> SRR1357389 3 0.0692 0.806 0.000 0.000 0.976 0.000 0.004 0.020
#> SRR1404227 3 0.1444 0.786 0.000 0.000 0.928 0.000 0.000 0.072
#> SRR1376830 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1500661 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1080294 2 0.1814 0.863 0.000 0.900 0.000 0.100 0.000 0.000
#> SRR1336314 4 0.0146 0.790 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1102152 3 0.3437 0.684 0.236 0.000 0.752 0.000 0.004 0.008
#> SRR1345244 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1478637 1 0.0622 0.788 0.980 0.000 0.012 0.000 0.000 0.008
#> SRR1443776 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1120939 3 0.2491 0.726 0.000 0.000 0.836 0.000 0.000 0.164
#> SRR1080117 3 0.1814 0.766 0.000 0.000 0.900 0.000 0.000 0.100
#> SRR1102899 2 0.0000 0.905 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091865 1 0.0547 0.788 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1361072 3 0.2100 0.769 0.112 0.000 0.884 0.000 0.004 0.000
#> SRR1487890 1 0.3371 0.790 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR1349456 6 0.0260 0.957 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1389384 1 0.3914 0.570 0.768 0.000 0.128 0.000 0.000 0.104
#> SRR1316096 2 0.1957 0.850 0.000 0.888 0.000 0.112 0.000 0.000
#> SRR1408512 3 0.3619 0.604 0.316 0.000 0.680 0.000 0.004 0.000
#> SRR1447547 5 0.4585 0.855 0.000 0.000 0.200 0.088 0.704 0.008
#> SRR1354053 4 0.1663 0.918 0.000 0.088 0.000 0.912 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.784 0.876 0.948 0.4147 0.564 0.564
#> 3 3 0.877 0.883 0.952 0.5706 0.687 0.491
#> 4 4 0.695 0.734 0.862 0.0578 0.885 0.705
#> 5 5 0.632 0.668 0.804 0.0538 0.930 0.789
#> 6 6 0.660 0.605 0.778 0.0675 0.917 0.722
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.000 0.973 1.000 0.000
#> SRR1349562 1 0.000 0.973 1.000 0.000
#> SRR1353376 2 0.000 0.875 0.000 1.000
#> SRR1499040 1 0.000 0.973 1.000 0.000
#> SRR1322312 1 0.000 0.973 1.000 0.000
#> SRR1324412 1 0.000 0.973 1.000 0.000
#> SRR1100991 1 0.000 0.973 1.000 0.000
#> SRR1349479 2 0.932 0.563 0.348 0.652
#> SRR1431248 1 0.850 0.535 0.724 0.276
#> SRR1405054 1 0.000 0.973 1.000 0.000
#> SRR1312266 1 0.000 0.973 1.000 0.000
#> SRR1409790 1 0.000 0.973 1.000 0.000
#> SRR1352507 1 0.000 0.973 1.000 0.000
#> SRR1383763 1 0.000 0.973 1.000 0.000
#> SRR1468314 2 0.000 0.875 0.000 1.000
#> SRR1473674 2 0.000 0.875 0.000 1.000
#> SRR1390499 1 0.000 0.973 1.000 0.000
#> SRR821043 2 0.000 0.875 0.000 1.000
#> SRR1455653 2 0.000 0.875 0.000 1.000
#> SRR1335236 2 0.000 0.875 0.000 1.000
#> SRR1095383 2 0.000 0.875 0.000 1.000
#> SRR1479489 1 0.000 0.973 1.000 0.000
#> SRR1310433 2 0.000 0.875 0.000 1.000
#> SRR1073435 2 0.929 0.570 0.344 0.656
#> SRR659649 1 0.000 0.973 1.000 0.000
#> SRR1395999 1 0.000 0.973 1.000 0.000
#> SRR1105248 2 0.952 0.521 0.372 0.628
#> SRR1338257 1 0.000 0.973 1.000 0.000
#> SRR1499395 1 0.000 0.973 1.000 0.000
#> SRR1350002 2 0.000 0.875 0.000 1.000
#> SRR1489757 1 0.000 0.973 1.000 0.000
#> SRR1414637 2 0.000 0.875 0.000 1.000
#> SRR1478113 2 0.000 0.875 0.000 1.000
#> SRR1322477 1 1.000 -0.222 0.504 0.496
#> SRR1478789 1 1.000 -0.180 0.504 0.496
#> SRR1414185 1 0.000 0.973 1.000 0.000
#> SRR1069141 2 0.000 0.875 0.000 1.000
#> SRR1376852 1 0.000 0.973 1.000 0.000
#> SRR1323491 1 0.000 0.973 1.000 0.000
#> SRR1338103 1 0.000 0.973 1.000 0.000
#> SRR1472012 1 0.000 0.973 1.000 0.000
#> SRR1340325 1 0.000 0.973 1.000 0.000
#> SRR1087321 1 0.584 0.806 0.860 0.140
#> SRR1488790 1 0.000 0.973 1.000 0.000
#> SRR1334866 1 0.000 0.973 1.000 0.000
#> SRR1089446 1 0.000 0.973 1.000 0.000
#> SRR1344445 1 0.000 0.973 1.000 0.000
#> SRR1412969 1 0.000 0.973 1.000 0.000
#> SRR1071668 1 0.000 0.973 1.000 0.000
#> SRR1075804 1 0.000 0.973 1.000 0.000
#> SRR1383283 2 0.929 0.570 0.344 0.656
#> SRR1350239 2 1.000 0.250 0.488 0.512
#> SRR1353878 1 0.000 0.973 1.000 0.000
#> SRR1375721 1 0.000 0.973 1.000 0.000
#> SRR1083983 1 0.000 0.973 1.000 0.000
#> SRR1090095 1 0.000 0.973 1.000 0.000
#> SRR1414792 1 0.000 0.973 1.000 0.000
#> SRR1075102 2 0.000 0.875 0.000 1.000
#> SRR1098737 1 0.000 0.973 1.000 0.000
#> SRR1349409 1 0.000 0.973 1.000 0.000
#> SRR1413008 2 1.000 0.250 0.488 0.512
#> SRR1407179 1 0.000 0.973 1.000 0.000
#> SRR1095913 1 0.563 0.818 0.868 0.132
#> SRR1403544 1 0.000 0.973 1.000 0.000
#> SRR1490546 1 0.000 0.973 1.000 0.000
#> SRR807971 1 0.000 0.973 1.000 0.000
#> SRR1436228 2 0.929 0.570 0.344 0.656
#> SRR1445218 2 0.000 0.875 0.000 1.000
#> SRR1485438 2 0.000 0.875 0.000 1.000
#> SRR1358143 1 0.000 0.973 1.000 0.000
#> SRR1328760 1 0.000 0.973 1.000 0.000
#> SRR1380806 1 0.000 0.973 1.000 0.000
#> SRR1379426 1 0.000 0.973 1.000 0.000
#> SRR1087007 1 0.000 0.973 1.000 0.000
#> SRR1086256 2 0.866 0.643 0.288 0.712
#> SRR1346734 2 0.000 0.875 0.000 1.000
#> SRR1414515 1 0.000 0.973 1.000 0.000
#> SRR1082151 2 0.000 0.875 0.000 1.000
#> SRR1349320 2 0.000 0.875 0.000 1.000
#> SRR1317554 2 0.000 0.875 0.000 1.000
#> SRR1076022 2 0.000 0.875 0.000 1.000
#> SRR1339573 1 0.000 0.973 1.000 0.000
#> SRR1455878 1 0.000 0.973 1.000 0.000
#> SRR1446203 1 0.000 0.973 1.000 0.000
#> SRR1387397 1 0.000 0.973 1.000 0.000
#> SRR1402590 1 0.000 0.973 1.000 0.000
#> SRR1317532 1 0.000 0.973 1.000 0.000
#> SRR1331488 2 1.000 0.237 0.492 0.508
#> SRR1499675 1 0.000 0.973 1.000 0.000
#> SRR1440467 1 0.584 0.806 0.860 0.140
#> SRR807995 2 0.000 0.875 0.000 1.000
#> SRR1476485 2 0.000 0.875 0.000 1.000
#> SRR1388214 1 0.000 0.973 1.000 0.000
#> SRR1456051 1 0.000 0.973 1.000 0.000
#> SRR1473275 1 0.000 0.973 1.000 0.000
#> SRR1444083 1 0.000 0.973 1.000 0.000
#> SRR1313807 2 0.932 0.563 0.348 0.652
#> SRR1470751 2 0.000 0.875 0.000 1.000
#> SRR1403434 1 0.584 0.806 0.860 0.140
#> SRR1390540 1 0.000 0.973 1.000 0.000
#> SRR1093861 2 0.000 0.875 0.000 1.000
#> SRR1325290 1 0.000 0.973 1.000 0.000
#> SRR1070689 1 0.000 0.973 1.000 0.000
#> SRR1384049 1 0.000 0.973 1.000 0.000
#> SRR1081184 1 0.000 0.973 1.000 0.000
#> SRR1324295 1 0.000 0.973 1.000 0.000
#> SRR1365313 1 0.714 0.719 0.804 0.196
#> SRR1321877 1 0.000 0.973 1.000 0.000
#> SRR815711 1 0.000 0.973 1.000 0.000
#> SRR1433476 2 0.929 0.570 0.344 0.656
#> SRR1101883 1 0.000 0.973 1.000 0.000
#> SRR1433729 2 0.760 0.715 0.220 0.780
#> SRR1341877 1 0.000 0.973 1.000 0.000
#> SRR1090556 1 0.000 0.973 1.000 0.000
#> SRR1357389 1 0.000 0.973 1.000 0.000
#> SRR1404227 1 0.000 0.973 1.000 0.000
#> SRR1376830 1 0.000 0.973 1.000 0.000
#> SRR1500661 1 0.000 0.973 1.000 0.000
#> SRR1080294 2 0.000 0.875 0.000 1.000
#> SRR1336314 2 0.000 0.875 0.000 1.000
#> SRR1102152 1 0.000 0.973 1.000 0.000
#> SRR1345244 1 0.000 0.973 1.000 0.000
#> SRR1478637 1 0.000 0.973 1.000 0.000
#> SRR1443776 1 0.000 0.973 1.000 0.000
#> SRR1120939 1 0.000 0.973 1.000 0.000
#> SRR1080117 1 0.000 0.973 1.000 0.000
#> SRR1102899 2 0.000 0.875 0.000 1.000
#> SRR1091865 1 0.000 0.973 1.000 0.000
#> SRR1361072 1 0.000 0.973 1.000 0.000
#> SRR1487890 1 0.000 0.973 1.000 0.000
#> SRR1349456 1 0.469 0.860 0.900 0.100
#> SRR1389384 2 0.000 0.875 0.000 1.000
#> SRR1316096 2 0.000 0.875 0.000 1.000
#> SRR1408512 1 0.000 0.973 1.000 0.000
#> SRR1447547 2 1.000 0.250 0.488 0.512
#> SRR1354053 2 0.000 0.875 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1353376 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1499040 1 0.3752 0.8055 0.856 0.000 0.144
#> SRR1322312 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1324412 3 0.0892 0.9116 0.020 0.000 0.980
#> SRR1100991 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1349479 3 0.6045 0.4414 0.000 0.380 0.620
#> SRR1431248 1 0.0661 0.9447 0.988 0.008 0.004
#> SRR1405054 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1312266 1 0.4555 0.7470 0.800 0.200 0.000
#> SRR1409790 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1352507 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1383763 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1468314 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1473674 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1390499 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR821043 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1455653 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1335236 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1095383 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1479489 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1310433 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1073435 2 0.5763 0.5943 0.276 0.716 0.008
#> SRR659649 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1105248 1 0.6721 0.3688 0.604 0.380 0.016
#> SRR1338257 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1499395 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1350002 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1489757 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1414637 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1478113 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1322477 1 0.3644 0.8324 0.872 0.124 0.004
#> SRR1478789 3 0.5926 0.4918 0.000 0.356 0.644
#> SRR1414185 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1069141 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1376852 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1338103 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1472012 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1340325 1 0.0424 0.9471 0.992 0.000 0.008
#> SRR1087321 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1334866 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1089446 3 0.1529 0.8986 0.000 0.040 0.960
#> SRR1344445 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1075804 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1383283 3 0.6180 0.3560 0.000 0.416 0.584
#> SRR1350239 1 0.6126 0.4578 0.644 0.352 0.004
#> SRR1353878 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1375721 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1083983 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1090095 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1075102 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1098737 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1349409 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1413008 1 0.6081 0.4758 0.652 0.344 0.004
#> SRR1407179 3 0.1860 0.8833 0.052 0.000 0.948
#> SRR1095913 1 0.9191 -0.0724 0.432 0.148 0.420
#> SRR1403544 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1490546 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR807971 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1436228 2 0.5815 0.5399 0.304 0.692 0.004
#> SRR1445218 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1485438 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1358143 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1328760 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1380806 1 0.2165 0.8984 0.936 0.000 0.064
#> SRR1379426 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1086256 2 0.0983 0.9442 0.016 0.980 0.004
#> SRR1346734 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1414515 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1082151 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1349320 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1317554 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1076022 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1339573 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1387397 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1402590 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1317532 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1331488 1 0.5016 0.6739 0.760 0.240 0.000
#> SRR1499675 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1440467 3 0.4887 0.7014 0.000 0.228 0.772
#> SRR807995 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1476485 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1388214 1 0.0747 0.9418 0.984 0.000 0.016
#> SRR1456051 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1444083 1 0.0747 0.9420 0.984 0.000 0.016
#> SRR1313807 3 0.6062 0.4327 0.000 0.384 0.616
#> SRR1470751 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1403434 3 0.4887 0.7014 0.000 0.228 0.772
#> SRR1390540 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1093861 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1325290 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1070689 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1384049 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1081184 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1365313 2 0.7029 0.0626 0.020 0.540 0.440
#> SRR1321877 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1433476 3 0.6252 0.2800 0.000 0.444 0.556
#> SRR1101883 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1433729 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1341877 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1090556 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1357389 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1404227 3 0.0892 0.9118 0.020 0.000 0.980
#> SRR1376830 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1080294 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1336314 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1102152 1 0.0592 0.9449 0.988 0.000 0.012
#> SRR1345244 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1478637 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1443776 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.9267 0.000 0.000 1.000
#> SRR1102899 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1091865 1 0.0237 0.9494 0.996 0.000 0.004
#> SRR1361072 1 0.0424 0.9471 0.992 0.000 0.008
#> SRR1487890 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1349456 3 0.0237 0.9243 0.000 0.004 0.996
#> SRR1389384 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1316096 2 0.0000 0.9629 0.000 1.000 0.000
#> SRR1408512 1 0.0000 0.9513 1.000 0.000 0.000
#> SRR1447547 1 0.5690 0.5889 0.708 0.288 0.004
#> SRR1354053 2 0.0000 0.9629 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.2281 0.888 0.904 0.096 0.000 0.000
#> SRR1349562 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1353376 4 0.1151 0.570 0.000 0.024 0.008 0.968
#> SRR1499040 1 0.3569 0.794 0.804 0.000 0.196 0.000
#> SRR1322312 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.3123 0.771 0.156 0.000 0.844 0.000
#> SRR1100991 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1349479 4 0.7106 0.347 0.000 0.148 0.324 0.528
#> SRR1431248 1 0.4456 0.849 0.804 0.148 0.044 0.004
#> SRR1405054 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1312266 1 0.4597 0.841 0.800 0.148 0.008 0.044
#> SRR1409790 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1352507 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1383763 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1468314 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1473674 2 0.3444 0.768 0.000 0.816 0.000 0.184
#> SRR1390499 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR821043 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1455653 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1335236 2 0.4961 0.754 0.000 0.552 0.000 0.448
#> SRR1095383 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1479489 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1310433 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1073435 4 0.7831 0.394 0.220 0.148 0.052 0.580
#> SRR659649 3 0.1059 0.914 0.012 0.016 0.972 0.000
#> SRR1395999 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.8512 0.362 0.260 0.156 0.076 0.508
#> SRR1338257 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1499395 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1350002 2 0.3444 0.768 0.000 0.816 0.000 0.184
#> SRR1489757 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1414637 4 0.7292 -0.658 0.080 0.440 0.024 0.456
#> SRR1478113 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1322477 1 0.4456 0.849 0.804 0.148 0.044 0.004
#> SRR1478789 3 0.8005 0.213 0.016 0.332 0.456 0.196
#> SRR1414185 3 0.0469 0.919 0.012 0.000 0.988 0.000
#> SRR1069141 2 0.4977 0.740 0.000 0.540 0.000 0.460
#> SRR1376852 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.4008 0.858 0.820 0.148 0.032 0.000
#> SRR1472012 1 0.3523 0.875 0.856 0.112 0.032 0.000
#> SRR1340325 1 0.1118 0.895 0.964 0.000 0.036 0.000
#> SRR1087321 3 0.3808 0.789 0.012 0.160 0.824 0.004
#> SRR1488790 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1334866 1 0.4188 0.853 0.812 0.148 0.040 0.000
#> SRR1089446 3 0.3880 0.798 0.020 0.136 0.836 0.008
#> SRR1344445 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1412969 3 0.1059 0.914 0.012 0.016 0.972 0.000
#> SRR1071668 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1075804 1 0.3024 0.868 0.852 0.148 0.000 0.000
#> SRR1383283 4 0.7092 0.350 0.000 0.148 0.320 0.532
#> SRR1350239 4 0.8353 0.357 0.276 0.156 0.060 0.508
#> SRR1353878 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1375721 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1083983 1 0.1059 0.903 0.972 0.012 0.016 0.000
#> SRR1090095 1 0.1940 0.893 0.924 0.076 0.000 0.000
#> SRR1414792 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1098737 1 0.3024 0.868 0.852 0.148 0.000 0.000
#> SRR1349409 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.8308 0.355 0.280 0.156 0.056 0.508
#> SRR1407179 3 0.2704 0.815 0.124 0.000 0.876 0.000
#> SRR1095913 1 0.8057 0.592 0.596 0.148 0.120 0.136
#> SRR1403544 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.3257 0.866 0.844 0.152 0.004 0.000
#> SRR807971 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1436228 1 0.7472 0.548 0.568 0.292 0.036 0.104
#> SRR1445218 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1485438 2 0.3486 0.769 0.000 0.812 0.000 0.188
#> SRR1358143 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1380806 1 0.3444 0.788 0.816 0.000 0.184 0.000
#> SRR1379426 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1087007 3 0.1059 0.914 0.012 0.016 0.972 0.000
#> SRR1086256 4 0.7512 0.280 0.236 0.152 0.028 0.584
#> SRR1346734 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0188 0.903 0.996 0.000 0.004 0.000
#> SRR1082151 2 0.4961 0.755 0.000 0.552 0.000 0.448
#> SRR1349320 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1076022 4 0.4697 -0.394 0.000 0.356 0.000 0.644
#> SRR1339573 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1455878 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1446203 3 0.1388 0.911 0.012 0.028 0.960 0.000
#> SRR1387397 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1402590 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.3962 0.859 0.820 0.152 0.028 0.000
#> SRR1331488 4 0.8326 0.161 0.372 0.152 0.044 0.432
#> SRR1499675 1 0.3958 0.860 0.824 0.144 0.032 0.000
#> SRR1440467 3 0.5392 0.104 0.012 0.000 0.528 0.460
#> SRR807995 2 0.3444 0.768 0.000 0.816 0.000 0.184
#> SRR1476485 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.4686 0.839 0.788 0.144 0.068 0.000
#> SRR1456051 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1444083 1 0.1716 0.864 0.936 0.000 0.064 0.000
#> SRR1313807 4 0.7106 0.347 0.000 0.148 0.324 0.528
#> SRR1470751 2 0.4967 0.751 0.000 0.548 0.000 0.452
#> SRR1403434 3 0.4485 0.625 0.012 0.000 0.740 0.248
#> SRR1390540 1 0.3300 0.869 0.848 0.144 0.008 0.000
#> SRR1093861 4 0.4977 -0.630 0.000 0.460 0.000 0.540
#> SRR1325290 1 0.4008 0.858 0.820 0.148 0.032 0.000
#> SRR1070689 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.0188 0.903 0.996 0.000 0.004 0.000
#> SRR1081184 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1365313 1 0.9193 0.232 0.408 0.204 0.292 0.096
#> SRR1321877 3 0.1388 0.911 0.012 0.028 0.960 0.000
#> SRR815711 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1433476 4 0.7077 0.352 0.000 0.148 0.316 0.536
#> SRR1101883 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1433729 4 0.4274 0.498 0.000 0.148 0.044 0.808
#> SRR1341877 1 0.3958 0.860 0.824 0.144 0.032 0.000
#> SRR1090556 1 0.4008 0.858 0.820 0.148 0.032 0.000
#> SRR1357389 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1404227 3 0.2919 0.855 0.060 0.044 0.896 0.000
#> SRR1376830 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1500661 1 0.0592 0.902 0.984 0.000 0.016 0.000
#> SRR1080294 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1336314 4 0.0000 0.581 0.000 0.000 0.000 1.000
#> SRR1102152 1 0.1118 0.898 0.964 0.000 0.036 0.000
#> SRR1345244 3 0.1388 0.911 0.012 0.028 0.960 0.000
#> SRR1478637 1 0.4274 0.851 0.808 0.148 0.044 0.000
#> SRR1443776 3 0.1388 0.911 0.012 0.028 0.960 0.000
#> SRR1120939 3 0.1059 0.914 0.012 0.016 0.972 0.000
#> SRR1080117 3 0.0707 0.923 0.020 0.000 0.980 0.000
#> SRR1102899 4 0.2530 0.399 0.000 0.112 0.000 0.888
#> SRR1091865 1 0.4188 0.853 0.812 0.148 0.040 0.000
#> SRR1361072 1 0.2399 0.895 0.920 0.048 0.032 0.000
#> SRR1487890 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.3271 0.824 0.012 0.132 0.856 0.000
#> SRR1389384 2 0.7126 0.619 0.068 0.480 0.024 0.428
#> SRR1316096 4 0.1474 0.510 0.000 0.052 0.000 0.948
#> SRR1408512 1 0.3913 0.860 0.824 0.148 0.028 0.000
#> SRR1447547 4 0.8369 0.348 0.288 0.152 0.060 0.500
#> SRR1354053 4 0.0000 0.581 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.2424 0.767940 0.868 0.000 0.000 0.000 0.132
#> SRR1349562 1 0.2813 0.710494 0.832 0.000 0.000 0.168 0.000
#> SRR1353376 5 0.3885 -0.091749 0.000 0.040 0.000 0.176 0.784
#> SRR1499040 3 0.6994 0.460543 0.232 0.000 0.552 0.156 0.060
#> SRR1322312 1 0.3731 0.716291 0.800 0.000 0.040 0.160 0.000
#> SRR1324412 3 0.1544 0.847004 0.068 0.000 0.932 0.000 0.000
#> SRR1100991 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1349479 5 0.2162 0.439789 0.000 0.012 0.064 0.008 0.916
#> SRR1431248 1 0.5295 0.646193 0.648 0.000 0.064 0.008 0.280
#> SRR1405054 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1312266 1 0.4040 0.694324 0.712 0.000 0.012 0.000 0.276
#> SRR1409790 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1352507 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1383763 1 0.6691 0.666736 0.616 0.000 0.088 0.160 0.136
#> SRR1468314 5 0.4394 -0.183733 0.000 0.048 0.000 0.220 0.732
#> SRR1473674 2 0.0000 0.770864 0.000 1.000 0.000 0.000 0.000
#> SRR1390499 1 0.2732 0.716100 0.840 0.000 0.000 0.160 0.000
#> SRR821043 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1455653 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1335236 2 0.0566 0.773610 0.000 0.984 0.000 0.012 0.004
#> SRR1095383 5 0.4461 -0.192196 0.000 0.052 0.000 0.220 0.728
#> SRR1479489 3 0.1043 0.861484 0.040 0.000 0.960 0.000 0.000
#> SRR1310433 5 0.6141 -0.333814 0.000 0.244 0.000 0.196 0.560
#> SRR1073435 5 0.1739 0.456726 0.024 0.000 0.032 0.004 0.940
#> SRR659649 3 0.1173 0.855718 0.012 0.000 0.964 0.020 0.004
#> SRR1395999 1 0.0162 0.777678 0.996 0.000 0.000 0.004 0.000
#> SRR1105248 5 0.4938 0.297987 0.332 0.000 0.028 0.008 0.632
#> SRR1338257 1 0.0000 0.778170 1.000 0.000 0.000 0.000 0.000
#> SRR1499395 3 0.0771 0.860695 0.020 0.000 0.976 0.004 0.000
#> SRR1350002 2 0.0000 0.770864 0.000 1.000 0.000 0.000 0.000
#> SRR1489757 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1414637 2 0.5384 0.665024 0.016 0.744 0.032 0.096 0.112
#> SRR1478113 4 0.4380 0.981539 0.000 0.008 0.000 0.616 0.376
#> SRR1322477 1 0.5295 0.645526 0.648 0.000 0.064 0.008 0.280
#> SRR1478789 3 0.7272 0.389290 0.068 0.140 0.544 0.008 0.240
#> SRR1414185 3 0.0693 0.857084 0.012 0.000 0.980 0.008 0.000
#> SRR1069141 2 0.1281 0.769026 0.000 0.956 0.000 0.012 0.032
#> SRR1376852 1 0.2471 0.728719 0.864 0.000 0.000 0.136 0.000
#> SRR1323491 1 0.2179 0.773426 0.896 0.000 0.000 0.004 0.100
#> SRR1338103 1 0.4475 0.681357 0.692 0.000 0.032 0.000 0.276
#> SRR1472012 1 0.3191 0.774381 0.860 0.000 0.052 0.004 0.084
#> SRR1340325 1 0.0794 0.780544 0.972 0.000 0.028 0.000 0.000
#> SRR1087321 3 0.5233 0.654032 0.000 0.000 0.684 0.148 0.168
#> SRR1488790 1 0.0162 0.778989 0.996 0.000 0.004 0.000 0.000
#> SRR1334866 1 0.5230 0.656744 0.660 0.000 0.064 0.008 0.268
#> SRR1089446 3 0.6253 0.218234 0.188 0.000 0.532 0.000 0.280
#> SRR1344445 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1412969 3 0.0451 0.852134 0.004 0.000 0.988 0.008 0.000
#> SRR1071668 3 0.1124 0.862396 0.036 0.000 0.960 0.000 0.004
#> SRR1075804 1 0.3814 0.699199 0.720 0.000 0.004 0.000 0.276
#> SRR1383283 5 0.1605 0.451342 0.000 0.012 0.040 0.004 0.944
#> SRR1350239 5 0.4984 0.268286 0.344 0.000 0.028 0.008 0.620
#> SRR1353878 1 0.0000 0.778170 1.000 0.000 0.000 0.000 0.000
#> SRR1375721 1 0.1894 0.775800 0.920 0.000 0.072 0.008 0.000
#> SRR1083983 1 0.1041 0.783258 0.964 0.000 0.032 0.000 0.004
#> SRR1090095 1 0.2852 0.757459 0.828 0.000 0.000 0.000 0.172
#> SRR1414792 1 0.0404 0.777285 0.988 0.000 0.000 0.012 0.000
#> SRR1075102 4 0.4380 0.981539 0.000 0.008 0.000 0.616 0.376
#> SRR1098737 1 0.3661 0.701158 0.724 0.000 0.000 0.000 0.276
#> SRR1349409 1 0.2773 0.713536 0.836 0.000 0.000 0.164 0.000
#> SRR1413008 5 0.5060 0.260773 0.344 0.000 0.032 0.008 0.616
#> SRR1407179 3 0.3419 0.731455 0.180 0.000 0.804 0.016 0.000
#> SRR1095913 1 0.5682 0.673823 0.668 0.000 0.132 0.016 0.184
#> SRR1403544 1 0.3106 0.734103 0.844 0.000 0.024 0.132 0.000
#> SRR1490546 1 0.4016 0.699344 0.716 0.000 0.012 0.000 0.272
#> SRR807971 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1436228 1 0.7153 0.513526 0.540 0.116 0.064 0.008 0.272
#> SRR1445218 2 0.6567 -0.153530 0.000 0.432 0.000 0.208 0.360
#> SRR1485438 2 0.0404 0.773392 0.000 0.988 0.000 0.012 0.000
#> SRR1358143 1 0.3875 0.707772 0.792 0.000 0.048 0.160 0.000
#> SRR1328760 1 0.0898 0.782204 0.972 0.000 0.020 0.008 0.000
#> SRR1380806 3 0.4392 0.455689 0.380 0.000 0.612 0.008 0.000
#> SRR1379426 3 0.0771 0.860588 0.020 0.000 0.976 0.004 0.000
#> SRR1087007 3 0.0693 0.857084 0.012 0.000 0.980 0.008 0.000
#> SRR1086256 1 0.5995 0.526886 0.576 0.016 0.064 0.008 0.336
#> SRR1346734 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1414515 1 0.1331 0.778418 0.952 0.000 0.040 0.008 0.000
#> SRR1082151 2 0.1518 0.771319 0.000 0.952 0.016 0.012 0.020
#> SRR1349320 4 0.4380 0.981539 0.000 0.008 0.000 0.616 0.376
#> SRR1317554 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1076022 2 0.5093 0.545524 0.000 0.696 0.000 0.180 0.124
#> SRR1339573 3 0.0963 0.862391 0.036 0.000 0.964 0.000 0.000
#> SRR1455878 1 0.0162 0.778989 0.996 0.000 0.004 0.000 0.000
#> SRR1446203 3 0.2930 0.787377 0.000 0.000 0.832 0.164 0.004
#> SRR1387397 1 0.1041 0.783046 0.964 0.000 0.032 0.004 0.000
#> SRR1402590 1 0.2773 0.710884 0.836 0.000 0.000 0.164 0.000
#> SRR1317532 1 0.4550 0.682145 0.692 0.000 0.028 0.004 0.276
#> SRR1331488 5 0.5028 -0.094219 0.444 0.000 0.032 0.000 0.524
#> SRR1499675 1 0.4425 0.723198 0.744 0.000 0.048 0.004 0.204
#> SRR1440467 3 0.4387 0.451493 0.000 0.000 0.640 0.012 0.348
#> SRR807995 2 0.0000 0.770864 0.000 1.000 0.000 0.000 0.000
#> SRR1476485 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1388214 1 0.4764 0.716494 0.716 0.000 0.052 0.008 0.224
#> SRR1456051 1 0.0404 0.777285 0.988 0.000 0.000 0.012 0.000
#> SRR1473275 3 0.1121 0.860483 0.044 0.000 0.956 0.000 0.000
#> SRR1444083 1 0.1410 0.767395 0.940 0.000 0.060 0.000 0.000
#> SRR1313807 5 0.1444 0.452995 0.000 0.012 0.040 0.000 0.948
#> SRR1470751 2 0.1518 0.771319 0.000 0.952 0.016 0.012 0.020
#> SRR1403434 3 0.4306 0.494021 0.000 0.000 0.660 0.012 0.328
#> SRR1390540 1 0.3554 0.737625 0.776 0.000 0.004 0.004 0.216
#> SRR1093861 2 0.1740 0.757184 0.000 0.932 0.000 0.012 0.056
#> SRR1325290 1 0.4204 0.729258 0.756 0.000 0.048 0.000 0.196
#> SRR1070689 1 0.2377 0.735399 0.872 0.000 0.000 0.128 0.000
#> SRR1384049 1 0.3774 0.722925 0.808 0.000 0.032 0.152 0.008
#> SRR1081184 1 0.2732 0.713584 0.840 0.000 0.000 0.160 0.000
#> SRR1324295 1 0.2773 0.710884 0.836 0.000 0.000 0.164 0.000
#> SRR1365313 1 0.6729 0.557007 0.564 0.036 0.112 0.008 0.280
#> SRR1321877 3 0.2930 0.787377 0.000 0.000 0.832 0.164 0.004
#> SRR815711 3 0.0898 0.860406 0.020 0.000 0.972 0.008 0.000
#> SRR1433476 5 0.1444 0.452995 0.000 0.012 0.040 0.000 0.948
#> SRR1101883 3 0.1043 0.861675 0.040 0.000 0.960 0.000 0.000
#> SRR1433729 5 0.1442 0.437202 0.004 0.012 0.032 0.000 0.952
#> SRR1341877 1 0.4218 0.733064 0.760 0.000 0.040 0.004 0.196
#> SRR1090556 1 0.4697 0.710043 0.720 0.000 0.048 0.008 0.224
#> SRR1357389 3 0.0609 0.860482 0.020 0.000 0.980 0.000 0.000
#> SRR1404227 3 0.4155 0.721866 0.140 0.000 0.796 0.016 0.048
#> SRR1376830 1 0.2561 0.726674 0.856 0.000 0.000 0.144 0.000
#> SRR1500661 1 0.0000 0.778170 1.000 0.000 0.000 0.000 0.000
#> SRR1080294 5 0.4333 -0.166141 0.000 0.048 0.000 0.212 0.740
#> SRR1336314 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
#> SRR1102152 1 0.2095 0.780761 0.920 0.000 0.060 0.012 0.008
#> SRR1345244 3 0.2930 0.787377 0.000 0.000 0.832 0.164 0.004
#> SRR1478637 1 0.5575 0.681968 0.680 0.000 0.128 0.016 0.176
#> SRR1443776 3 0.2930 0.787377 0.000 0.000 0.832 0.164 0.004
#> SRR1120939 3 0.1908 0.818827 0.000 0.000 0.908 0.092 0.000
#> SRR1080117 3 0.0771 0.860588 0.020 0.000 0.976 0.004 0.000
#> SRR1102899 2 0.6434 -0.000154 0.000 0.432 0.000 0.176 0.392
#> SRR1091865 1 0.5053 0.664539 0.668 0.000 0.060 0.004 0.268
#> SRR1361072 1 0.3678 0.756113 0.804 0.000 0.020 0.008 0.168
#> SRR1487890 1 0.3731 0.710214 0.800 0.000 0.040 0.160 0.000
#> SRR1349456 3 0.5198 0.659608 0.000 0.000 0.688 0.148 0.164
#> SRR1389384 2 0.6445 0.380525 0.100 0.644 0.052 0.012 0.192
#> SRR1316096 2 0.6456 -0.038420 0.000 0.468 0.000 0.192 0.340
#> SRR1408512 1 0.4558 0.699945 0.708 0.000 0.036 0.004 0.252
#> SRR1447547 5 0.5022 0.273062 0.332 0.000 0.048 0.000 0.620
#> SRR1354053 4 0.4327 0.992152 0.000 0.008 0.000 0.632 0.360
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.3999 -0.4160 0.500 0.000 0.000 0.000 0.496 0.004
#> SRR1349562 1 0.2491 0.7112 0.868 0.000 0.000 0.000 0.020 0.112
#> SRR1353376 6 0.4947 0.6349 0.000 0.008 0.000 0.416 0.048 0.528
#> SRR1499040 3 0.6089 0.2575 0.352 0.000 0.488 0.000 0.128 0.032
#> SRR1322312 1 0.3058 0.7138 0.848 0.000 0.020 0.000 0.024 0.108
#> SRR1324412 3 0.1918 0.7935 0.088 0.000 0.904 0.000 0.008 0.000
#> SRR1100991 3 0.0547 0.8340 0.020 0.000 0.980 0.000 0.000 0.000
#> SRR1349479 6 0.5225 0.8197 0.000 0.008 0.004 0.240 0.112 0.636
#> SRR1431248 5 0.5021 0.5880 0.288 0.000 0.012 0.004 0.632 0.064
#> SRR1405054 3 0.0777 0.8326 0.024 0.000 0.972 0.000 0.004 0.000
#> SRR1312266 5 0.4076 0.5575 0.396 0.000 0.000 0.000 0.592 0.012
#> SRR1409790 3 0.0405 0.8346 0.008 0.000 0.988 0.000 0.004 0.000
#> SRR1352507 3 0.0692 0.8334 0.020 0.000 0.976 0.000 0.004 0.000
#> SRR1383763 1 0.4640 0.6680 0.744 0.000 0.044 0.000 0.092 0.120
#> SRR1468314 4 0.4326 -0.4941 0.000 0.008 0.000 0.496 0.008 0.488
#> SRR1473674 2 0.0146 0.8294 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1390499 1 0.2573 0.7114 0.864 0.000 0.000 0.000 0.024 0.112
#> SRR821043 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1455653 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1335236 2 0.0790 0.8347 0.000 0.968 0.000 0.032 0.000 0.000
#> SRR1095383 4 0.4326 -0.4941 0.000 0.008 0.000 0.496 0.008 0.488
#> SRR1479489 3 0.1010 0.8299 0.036 0.000 0.960 0.000 0.004 0.000
#> SRR1310433 4 0.5523 -0.2396 0.000 0.140 0.000 0.500 0.000 0.360
#> SRR1073435 5 0.6281 -0.0828 0.004 0.004 0.012 0.300 0.488 0.192
#> SRR659649 3 0.1738 0.8308 0.000 0.004 0.928 0.000 0.016 0.052
#> SRR1395999 1 0.0603 0.7360 0.980 0.000 0.000 0.000 0.016 0.004
#> SRR1105248 5 0.5859 0.2380 0.032 0.000 0.008 0.252 0.596 0.112
#> SRR1338257 1 0.1806 0.7019 0.908 0.000 0.000 0.000 0.088 0.004
#> SRR1499395 3 0.0508 0.8356 0.004 0.000 0.984 0.000 0.000 0.012
#> SRR1350002 2 0.0146 0.8294 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1489757 3 0.0405 0.8346 0.008 0.000 0.988 0.000 0.004 0.000
#> SRR1414637 2 0.5590 0.6525 0.024 0.668 0.000 0.128 0.156 0.024
#> SRR1478113 4 0.0146 0.7529 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1322477 5 0.4985 0.5886 0.280 0.000 0.012 0.004 0.640 0.064
#> SRR1478789 3 0.7790 0.4527 0.088 0.112 0.500 0.036 0.220 0.044
#> SRR1414185 3 0.1442 0.8327 0.000 0.004 0.944 0.000 0.012 0.040
#> SRR1069141 2 0.1471 0.8212 0.000 0.932 0.000 0.064 0.004 0.000
#> SRR1376852 1 0.2118 0.7233 0.888 0.000 0.000 0.000 0.008 0.104
#> SRR1323491 1 0.3868 -0.3952 0.508 0.000 0.000 0.000 0.492 0.000
#> SRR1338103 1 0.4655 0.0519 0.576 0.000 0.008 0.000 0.384 0.032
#> SRR1472012 1 0.2568 0.7023 0.876 0.000 0.012 0.000 0.096 0.016
#> SRR1340325 1 0.1549 0.7376 0.936 0.000 0.020 0.000 0.044 0.000
#> SRR1087321 3 0.5331 0.6312 0.000 0.000 0.580 0.000 0.268 0.152
#> SRR1488790 1 0.1010 0.7329 0.960 0.000 0.000 0.000 0.036 0.004
#> SRR1334866 1 0.4842 0.4672 0.648 0.000 0.012 0.004 0.284 0.052
#> SRR1089446 1 0.7670 0.0329 0.372 0.004 0.368 0.100 0.108 0.048
#> SRR1344445 3 0.0363 0.8348 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1412969 3 0.1864 0.8284 0.000 0.004 0.924 0.000 0.032 0.040
#> SRR1071668 3 0.0653 0.8359 0.004 0.000 0.980 0.000 0.004 0.012
#> SRR1075804 5 0.3747 0.5597 0.396 0.000 0.000 0.000 0.604 0.000
#> SRR1383283 6 0.5558 0.8332 0.000 0.008 0.012 0.276 0.108 0.596
#> SRR1350239 5 0.5421 0.2560 0.032 0.000 0.000 0.252 0.624 0.092
#> SRR1353878 1 0.0653 0.7407 0.980 0.000 0.004 0.000 0.004 0.012
#> SRR1375721 1 0.2811 0.7233 0.872 0.000 0.032 0.000 0.076 0.020
#> SRR1083983 1 0.1410 0.7348 0.944 0.000 0.008 0.000 0.044 0.004
#> SRR1090095 5 0.3867 0.4186 0.488 0.000 0.000 0.000 0.512 0.000
#> SRR1414792 1 0.0713 0.7372 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1075102 4 0.0146 0.7529 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1098737 5 0.3774 0.5395 0.408 0.000 0.000 0.000 0.592 0.000
#> SRR1349409 1 0.2527 0.7140 0.868 0.000 0.000 0.000 0.024 0.108
#> SRR1413008 5 0.5421 0.2560 0.032 0.000 0.000 0.252 0.624 0.092
#> SRR1407179 3 0.4682 0.6871 0.172 0.004 0.728 0.000 0.068 0.028
#> SRR1095913 1 0.5461 0.5003 0.644 0.000 0.048 0.016 0.248 0.044
#> SRR1403544 1 0.1679 0.7407 0.936 0.000 0.012 0.000 0.016 0.036
#> SRR1490546 5 0.3823 0.5012 0.436 0.000 0.000 0.000 0.564 0.000
#> SRR807971 3 0.0405 0.8346 0.008 0.000 0.988 0.000 0.004 0.000
#> SRR1436228 1 0.7024 -0.1369 0.412 0.092 0.012 0.020 0.400 0.064
#> SRR1445218 4 0.4184 -0.0900 0.000 0.488 0.000 0.500 0.000 0.012
#> SRR1485438 2 0.0363 0.8315 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1358143 1 0.3217 0.7067 0.840 0.000 0.024 0.000 0.028 0.108
#> SRR1328760 1 0.2195 0.7330 0.904 0.000 0.012 0.000 0.068 0.016
#> SRR1380806 3 0.4563 0.4169 0.348 0.000 0.604 0.000 0.048 0.000
#> SRR1379426 3 0.1780 0.8289 0.004 0.004 0.932 0.000 0.036 0.024
#> SRR1087007 3 0.1536 0.8311 0.000 0.004 0.940 0.000 0.016 0.040
#> SRR1086256 5 0.5804 0.5926 0.216 0.012 0.012 0.048 0.648 0.064
#> SRR1346734 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1414515 1 0.1321 0.7404 0.952 0.000 0.024 0.000 0.020 0.004
#> SRR1082151 2 0.1080 0.8350 0.000 0.960 0.000 0.032 0.004 0.004
#> SRR1349320 4 0.0146 0.7529 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1317554 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1076022 2 0.3230 0.6955 0.000 0.776 0.000 0.212 0.000 0.012
#> SRR1339573 3 0.0260 0.8346 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1455878 1 0.1088 0.7371 0.960 0.000 0.000 0.000 0.024 0.016
#> SRR1446203 3 0.5118 0.6998 0.000 0.004 0.640 0.000 0.148 0.208
#> SRR1387397 1 0.1838 0.7365 0.928 0.000 0.012 0.000 0.040 0.020
#> SRR1402590 1 0.2212 0.7159 0.880 0.000 0.000 0.000 0.008 0.112
#> SRR1317532 5 0.3789 0.5272 0.416 0.000 0.000 0.000 0.584 0.000
#> SRR1331488 5 0.5516 0.4726 0.132 0.000 0.000 0.224 0.620 0.024
#> SRR1499675 1 0.3516 0.6358 0.792 0.000 0.012 0.000 0.172 0.024
#> SRR1440467 3 0.5752 0.5438 0.000 0.004 0.624 0.208 0.040 0.124
#> SRR807995 2 0.0146 0.8294 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1476485 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388214 1 0.3979 0.1907 0.628 0.000 0.012 0.000 0.360 0.000
#> SRR1456051 1 0.0520 0.7382 0.984 0.000 0.000 0.000 0.008 0.008
#> SRR1473275 3 0.1408 0.8323 0.020 0.000 0.944 0.000 0.036 0.000
#> SRR1444083 1 0.2383 0.7290 0.900 0.000 0.052 0.000 0.028 0.020
#> SRR1313807 6 0.5466 0.8356 0.000 0.008 0.012 0.256 0.108 0.616
#> SRR1470751 2 0.1080 0.8350 0.000 0.960 0.000 0.032 0.004 0.004
#> SRR1403434 3 0.5549 0.5784 0.000 0.004 0.648 0.192 0.036 0.120
#> SRR1390540 5 0.3857 0.4398 0.468 0.000 0.000 0.000 0.532 0.000
#> SRR1093861 2 0.1141 0.8292 0.000 0.948 0.000 0.052 0.000 0.000
#> SRR1325290 1 0.3392 0.6541 0.820 0.000 0.012 0.000 0.128 0.040
#> SRR1070689 1 0.2560 0.7229 0.872 0.000 0.000 0.000 0.036 0.092
#> SRR1384049 1 0.2822 0.7137 0.856 0.000 0.004 0.000 0.032 0.108
#> SRR1081184 1 0.2500 0.7135 0.868 0.000 0.004 0.000 0.012 0.116
#> SRR1324295 1 0.2491 0.7112 0.868 0.000 0.000 0.000 0.020 0.112
#> SRR1365313 1 0.6156 0.4575 0.620 0.028 0.052 0.016 0.236 0.048
#> SRR1321877 3 0.5118 0.6998 0.000 0.004 0.640 0.000 0.148 0.208
#> SRR815711 3 0.0922 0.8352 0.004 0.004 0.968 0.000 0.000 0.024
#> SRR1433476 6 0.5374 0.8370 0.000 0.008 0.008 0.256 0.108 0.620
#> SRR1101883 3 0.1663 0.7956 0.088 0.000 0.912 0.000 0.000 0.000
#> SRR1433729 6 0.5987 0.7950 0.000 0.008 0.008 0.296 0.164 0.524
#> SRR1341877 1 0.3678 0.5572 0.748 0.000 0.008 0.000 0.228 0.016
#> SRR1090556 1 0.3954 0.4759 0.688 0.000 0.008 0.000 0.292 0.012
#> SRR1357389 3 0.0146 0.8343 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1404227 3 0.5634 0.5958 0.164 0.004 0.636 0.004 0.172 0.020
#> SRR1376830 1 0.2558 0.7170 0.868 0.000 0.000 0.000 0.028 0.104
#> SRR1500661 1 0.1049 0.7341 0.960 0.000 0.000 0.000 0.032 0.008
#> SRR1080294 6 0.4490 0.4583 0.000 0.008 0.000 0.472 0.016 0.504
#> SRR1336314 4 0.0260 0.7475 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1102152 1 0.2799 0.7117 0.852 0.000 0.012 0.000 0.124 0.012
#> SRR1345244 3 0.5118 0.6998 0.000 0.004 0.640 0.000 0.148 0.208
#> SRR1478637 1 0.4959 0.5511 0.688 0.000 0.040 0.008 0.224 0.040
#> SRR1443776 3 0.5118 0.6998 0.000 0.004 0.640 0.000 0.148 0.208
#> SRR1120939 3 0.3923 0.7733 0.000 0.004 0.772 0.000 0.080 0.144
#> SRR1080117 3 0.0837 0.8349 0.000 0.004 0.972 0.000 0.004 0.020
#> SRR1102899 2 0.5576 0.1330 0.000 0.480 0.000 0.376 0.000 0.144
#> SRR1091865 1 0.4060 0.5913 0.752 0.000 0.012 0.000 0.188 0.048
#> SRR1361072 1 0.3774 0.3016 0.664 0.000 0.008 0.000 0.328 0.000
#> SRR1487890 1 0.2889 0.7088 0.852 0.000 0.020 0.000 0.012 0.116
#> SRR1349456 3 0.5258 0.6452 0.000 0.000 0.596 0.000 0.252 0.152
#> SRR1389384 2 0.5221 0.6169 0.020 0.700 0.000 0.044 0.180 0.056
#> SRR1316096 2 0.4175 0.1162 0.000 0.524 0.000 0.464 0.000 0.012
#> SRR1408512 1 0.3747 0.0899 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1447547 5 0.6325 0.3180 0.056 0.000 0.016 0.248 0.572 0.108
#> SRR1354053 4 0.0000 0.7545 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17331 rows and 136 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.633 0.862 0.937 0.4786 0.521 0.521
#> 3 3 0.619 0.816 0.896 0.3365 0.797 0.629
#> 4 4 0.829 0.835 0.930 0.1487 0.811 0.543
#> 5 5 0.722 0.713 0.854 0.0664 0.844 0.513
#> 6 6 0.775 0.716 0.862 0.0445 0.925 0.680
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR815140 1 0.0000 0.9166 1.000 0.000
#> SRR1349562 1 0.0000 0.9166 1.000 0.000
#> SRR1353376 2 0.5519 0.8439 0.128 0.872
#> SRR1499040 1 0.7219 0.7745 0.800 0.200
#> SRR1322312 1 0.0000 0.9166 1.000 0.000
#> SRR1324412 1 0.5946 0.8250 0.856 0.144
#> SRR1100991 1 0.9580 0.4955 0.620 0.380
#> SRR1349479 2 0.0000 0.9372 0.000 1.000
#> SRR1431248 2 0.6623 0.7982 0.172 0.828
#> SRR1405054 1 0.8813 0.6481 0.700 0.300
#> SRR1312266 1 0.0000 0.9166 1.000 0.000
#> SRR1409790 1 0.9954 0.2792 0.540 0.460
#> SRR1352507 2 0.5178 0.8405 0.116 0.884
#> SRR1383763 1 0.0000 0.9166 1.000 0.000
#> SRR1468314 2 0.0000 0.9372 0.000 1.000
#> SRR1473674 2 0.0000 0.9372 0.000 1.000
#> SRR1390499 1 0.0000 0.9166 1.000 0.000
#> SRR821043 2 0.0672 0.9334 0.008 0.992
#> SRR1455653 2 0.2043 0.9198 0.032 0.968
#> SRR1335236 2 0.0000 0.9372 0.000 1.000
#> SRR1095383 2 0.0000 0.9372 0.000 1.000
#> SRR1479489 1 0.7528 0.7595 0.784 0.216
#> SRR1310433 2 0.0000 0.9372 0.000 1.000
#> SRR1073435 2 0.5059 0.8588 0.112 0.888
#> SRR659649 2 0.0000 0.9372 0.000 1.000
#> SRR1395999 1 0.0000 0.9166 1.000 0.000
#> SRR1105248 2 0.7139 0.7705 0.196 0.804
#> SRR1338257 1 0.0000 0.9166 1.000 0.000
#> SRR1499395 2 0.0000 0.9372 0.000 1.000
#> SRR1350002 2 0.0000 0.9372 0.000 1.000
#> SRR1489757 2 0.5629 0.8130 0.132 0.868
#> SRR1414637 2 0.7219 0.7655 0.200 0.800
#> SRR1478113 2 0.7453 0.7506 0.212 0.788
#> SRR1322477 2 0.3733 0.8926 0.072 0.928
#> SRR1478789 2 0.0000 0.9372 0.000 1.000
#> SRR1414185 2 0.0000 0.9372 0.000 1.000
#> SRR1069141 2 0.0000 0.9372 0.000 1.000
#> SRR1376852 1 0.0000 0.9166 1.000 0.000
#> SRR1323491 1 0.0000 0.9166 1.000 0.000
#> SRR1338103 1 0.0000 0.9166 1.000 0.000
#> SRR1472012 1 0.8909 0.6349 0.692 0.308
#> SRR1340325 1 0.0000 0.9166 1.000 0.000
#> SRR1087321 2 0.0000 0.9372 0.000 1.000
#> SRR1488790 1 0.0000 0.9166 1.000 0.000
#> SRR1334866 2 0.0000 0.9372 0.000 1.000
#> SRR1089446 2 0.0000 0.9372 0.000 1.000
#> SRR1344445 2 0.1633 0.9215 0.024 0.976
#> SRR1412969 2 0.0000 0.9372 0.000 1.000
#> SRR1071668 2 0.0000 0.9372 0.000 1.000
#> SRR1075804 1 0.0000 0.9166 1.000 0.000
#> SRR1383283 2 0.0000 0.9372 0.000 1.000
#> SRR1350239 2 0.7219 0.7655 0.200 0.800
#> SRR1353878 1 0.0000 0.9166 1.000 0.000
#> SRR1375721 1 0.7219 0.7745 0.800 0.200
#> SRR1083983 1 0.9209 0.5824 0.664 0.336
#> SRR1090095 1 0.0000 0.9166 1.000 0.000
#> SRR1414792 1 0.0000 0.9166 1.000 0.000
#> SRR1075102 2 0.7219 0.7655 0.200 0.800
#> SRR1098737 1 0.0000 0.9166 1.000 0.000
#> SRR1349409 1 0.0000 0.9166 1.000 0.000
#> SRR1413008 2 0.7219 0.7655 0.200 0.800
#> SRR1407179 2 0.0000 0.9372 0.000 1.000
#> SRR1095913 2 0.0000 0.9372 0.000 1.000
#> SRR1403544 1 0.0376 0.9149 0.996 0.004
#> SRR1490546 1 0.0000 0.9166 1.000 0.000
#> SRR807971 2 0.0672 0.9325 0.008 0.992
#> SRR1436228 2 0.0000 0.9372 0.000 1.000
#> SRR1445218 2 0.0000 0.9372 0.000 1.000
#> SRR1485438 2 0.0000 0.9372 0.000 1.000
#> SRR1358143 1 0.0000 0.9166 1.000 0.000
#> SRR1328760 1 0.8016 0.7284 0.756 0.244
#> SRR1380806 1 0.6438 0.8082 0.836 0.164
#> SRR1379426 2 0.0000 0.9372 0.000 1.000
#> SRR1087007 2 0.0000 0.9372 0.000 1.000
#> SRR1086256 2 0.0000 0.9372 0.000 1.000
#> SRR1346734 2 0.3879 0.8893 0.076 0.924
#> SRR1414515 1 0.7056 0.7818 0.808 0.192
#> SRR1082151 2 0.0000 0.9372 0.000 1.000
#> SRR1349320 2 0.7219 0.7655 0.200 0.800
#> SRR1317554 2 0.0672 0.9334 0.008 0.992
#> SRR1076022 2 0.0000 0.9372 0.000 1.000
#> SRR1339573 2 0.0000 0.9372 0.000 1.000
#> SRR1455878 1 0.0000 0.9166 1.000 0.000
#> SRR1446203 2 0.0000 0.9372 0.000 1.000
#> SRR1387397 1 0.6887 0.7891 0.816 0.184
#> SRR1402590 1 0.0000 0.9166 1.000 0.000
#> SRR1317532 1 0.0376 0.9147 0.996 0.004
#> SRR1331488 1 0.4939 0.8365 0.892 0.108
#> SRR1499675 2 0.6148 0.8197 0.152 0.848
#> SRR1440467 2 0.0000 0.9372 0.000 1.000
#> SRR807995 2 0.0000 0.9372 0.000 1.000
#> SRR1476485 2 0.2236 0.9174 0.036 0.964
#> SRR1388214 1 0.5178 0.8369 0.884 0.116
#> SRR1456051 1 0.0000 0.9166 1.000 0.000
#> SRR1473275 2 0.8016 0.6335 0.244 0.756
#> SRR1444083 1 0.1843 0.9029 0.972 0.028
#> SRR1313807 2 0.0000 0.9372 0.000 1.000
#> SRR1470751 2 0.3114 0.9049 0.056 0.944
#> SRR1403434 2 0.0000 0.9372 0.000 1.000
#> SRR1390540 1 0.0000 0.9166 1.000 0.000
#> SRR1093861 2 0.0000 0.9372 0.000 1.000
#> SRR1325290 2 0.9933 0.0657 0.452 0.548
#> SRR1070689 1 0.0000 0.9166 1.000 0.000
#> SRR1384049 1 0.0000 0.9166 1.000 0.000
#> SRR1081184 1 0.0000 0.9166 1.000 0.000
#> SRR1324295 1 0.0000 0.9166 1.000 0.000
#> SRR1365313 2 0.0000 0.9372 0.000 1.000
#> SRR1321877 2 0.0000 0.9372 0.000 1.000
#> SRR815711 2 0.0000 0.9372 0.000 1.000
#> SRR1433476 2 0.0000 0.9372 0.000 1.000
#> SRR1101883 2 0.0000 0.9372 0.000 1.000
#> SRR1433729 2 0.0000 0.9372 0.000 1.000
#> SRR1341877 1 0.9686 0.3054 0.604 0.396
#> SRR1090556 2 0.6438 0.8083 0.164 0.836
#> SRR1357389 2 0.0000 0.9372 0.000 1.000
#> SRR1404227 2 0.0000 0.9372 0.000 1.000
#> SRR1376830 1 0.0000 0.9166 1.000 0.000
#> SRR1500661 1 0.0000 0.9166 1.000 0.000
#> SRR1080294 2 0.0000 0.9372 0.000 1.000
#> SRR1336314 2 0.7056 0.7754 0.192 0.808
#> SRR1102152 2 0.9909 0.0651 0.444 0.556
#> SRR1345244 2 0.0000 0.9372 0.000 1.000
#> SRR1478637 2 0.0000 0.9372 0.000 1.000
#> SRR1443776 2 0.0000 0.9372 0.000 1.000
#> SRR1120939 2 0.0000 0.9372 0.000 1.000
#> SRR1080117 2 0.0000 0.9372 0.000 1.000
#> SRR1102899 2 0.0000 0.9372 0.000 1.000
#> SRR1091865 2 0.9896 0.0812 0.440 0.560
#> SRR1361072 1 0.0000 0.9166 1.000 0.000
#> SRR1487890 1 0.0000 0.9166 1.000 0.000
#> SRR1349456 2 0.0000 0.9372 0.000 1.000
#> SRR1389384 2 0.0000 0.9372 0.000 1.000
#> SRR1316096 2 0.0000 0.9372 0.000 1.000
#> SRR1408512 1 0.2778 0.8888 0.952 0.048
#> SRR1447547 2 0.2948 0.9070 0.052 0.948
#> SRR1354053 2 0.0672 0.9334 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR815140 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1349562 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1353376 2 0.3412 0.89842 0.124 0.876 0.000
#> SRR1499040 1 0.4974 0.73752 0.764 0.000 0.236
#> SRR1322312 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1324412 1 0.5200 0.78218 0.796 0.020 0.184
#> SRR1100991 1 0.6140 0.45539 0.596 0.000 0.404
#> SRR1349479 3 0.1411 0.87318 0.000 0.036 0.964
#> SRR1431248 2 0.5791 0.86143 0.148 0.792 0.060
#> SRR1405054 1 0.4002 0.80996 0.840 0.000 0.160
#> SRR1312266 2 0.4399 0.84351 0.188 0.812 0.000
#> SRR1409790 3 0.4062 0.72727 0.164 0.000 0.836
#> SRR1352507 3 0.4504 0.69192 0.196 0.000 0.804
#> SRR1383763 1 0.1860 0.86599 0.948 0.052 0.000
#> SRR1468314 3 0.4178 0.79665 0.000 0.172 0.828
#> SRR1473674 3 0.4399 0.81924 0.000 0.188 0.812
#> SRR1390499 1 0.1163 0.88090 0.972 0.028 0.000
#> SRR821043 2 0.0892 0.87834 0.020 0.980 0.000
#> SRR1455653 2 0.1289 0.88365 0.032 0.968 0.000
#> SRR1335236 3 0.3816 0.83544 0.000 0.148 0.852
#> SRR1095383 3 0.5905 0.63179 0.000 0.352 0.648
#> SRR1479489 1 0.4178 0.80107 0.828 0.000 0.172
#> SRR1310433 3 0.4605 0.80929 0.000 0.204 0.796
#> SRR1073435 3 0.9069 -0.08810 0.136 0.424 0.440
#> SRR659649 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1395999 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1105248 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1338257 1 0.3816 0.77714 0.852 0.148 0.000
#> SRR1499395 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1350002 3 0.4399 0.81976 0.000 0.188 0.812
#> SRR1489757 3 0.0237 0.87766 0.004 0.000 0.996
#> SRR1414637 2 0.0592 0.87381 0.012 0.988 0.000
#> SRR1478113 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1322477 2 0.8091 0.39768 0.088 0.592 0.320
#> SRR1478789 3 0.3267 0.84763 0.000 0.116 0.884
#> SRR1414185 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1069141 3 0.4291 0.82313 0.000 0.180 0.820
#> SRR1376852 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1323491 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1338103 1 0.5291 0.56723 0.732 0.268 0.000
#> SRR1472012 1 0.2496 0.86877 0.928 0.004 0.068
#> SRR1340325 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1087321 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1488790 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1334866 3 0.4291 0.82429 0.000 0.180 0.820
#> SRR1089446 3 0.1163 0.86555 0.028 0.000 0.972
#> SRR1344445 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1412969 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1071668 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1075804 2 0.4555 0.83122 0.200 0.800 0.000
#> SRR1383283 3 0.1753 0.87016 0.000 0.048 0.952
#> SRR1350239 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1353878 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1375721 1 0.3879 0.81562 0.848 0.000 0.152
#> SRR1083983 1 0.4121 0.80620 0.832 0.000 0.168
#> SRR1090095 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1414792 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1075102 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1098737 2 0.4291 0.85448 0.180 0.820 0.000
#> SRR1349409 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1413008 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1407179 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1095913 3 0.2066 0.87118 0.000 0.060 0.940
#> SRR1403544 1 0.2959 0.84937 0.900 0.000 0.100
#> SRR1490546 1 0.0892 0.88447 0.980 0.020 0.000
#> SRR807971 3 0.0237 0.87785 0.004 0.000 0.996
#> SRR1436228 3 0.5913 0.80107 0.068 0.144 0.788
#> SRR1445218 2 0.4702 0.58555 0.000 0.788 0.212
#> SRR1485438 3 0.3879 0.83436 0.000 0.152 0.848
#> SRR1358143 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1328760 1 0.4178 0.80166 0.828 0.000 0.172
#> SRR1380806 1 0.3551 0.82753 0.868 0.000 0.132
#> SRR1379426 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1087007 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1086256 3 0.4931 0.78760 0.000 0.232 0.768
#> SRR1346734 2 0.2448 0.89632 0.076 0.924 0.000
#> SRR1414515 1 0.3551 0.82753 0.868 0.000 0.132
#> SRR1082151 3 0.6026 0.60177 0.000 0.376 0.624
#> SRR1349320 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1317554 2 0.1163 0.88208 0.028 0.972 0.000
#> SRR1076022 3 0.5926 0.63596 0.000 0.356 0.644
#> SRR1339573 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1455878 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1446203 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1387397 1 0.2878 0.85181 0.904 0.000 0.096
#> SRR1402590 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1317532 1 0.1643 0.86842 0.956 0.044 0.000
#> SRR1331488 2 0.3551 0.89704 0.132 0.868 0.000
#> SRR1499675 3 0.7451 0.64677 0.156 0.144 0.700
#> SRR1440467 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR807995 3 0.4750 0.80095 0.000 0.216 0.784
#> SRR1476485 2 0.2625 0.89763 0.084 0.916 0.000
#> SRR1388214 1 0.0424 0.89255 0.992 0.000 0.008
#> SRR1456051 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1473275 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1444083 1 0.4634 0.80426 0.824 0.012 0.164
#> SRR1313807 3 0.1753 0.87016 0.000 0.048 0.952
#> SRR1470751 2 0.0000 0.86513 0.000 1.000 0.000
#> SRR1403434 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1390540 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1093861 3 0.4002 0.83139 0.000 0.160 0.840
#> SRR1325290 1 0.6662 0.64939 0.736 0.072 0.192
#> SRR1070689 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1384049 1 0.2261 0.85376 0.932 0.068 0.000
#> SRR1081184 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1324295 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1365313 3 0.2959 0.85469 0.000 0.100 0.900
#> SRR1321877 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR815711 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1433476 3 0.3619 0.82257 0.000 0.136 0.864
#> SRR1101883 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1433729 3 0.4702 0.79592 0.000 0.212 0.788
#> SRR1341877 1 0.5940 0.65078 0.760 0.204 0.036
#> SRR1090556 3 0.6516 0.08262 0.480 0.004 0.516
#> SRR1357389 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1404227 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1376830 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1500661 1 0.0000 0.89418 1.000 0.000 0.000
#> SRR1080294 3 0.3412 0.83138 0.000 0.124 0.876
#> SRR1336314 2 0.0237 0.86843 0.004 0.996 0.000
#> SRR1102152 3 0.6489 -0.00367 0.456 0.004 0.540
#> SRR1345244 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1478637 3 0.2878 0.85628 0.000 0.096 0.904
#> SRR1443776 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1120939 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1080117 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1102899 3 0.4346 0.82131 0.000 0.184 0.816
#> SRR1091865 1 0.8732 0.33646 0.552 0.132 0.316
#> SRR1361072 1 0.0592 0.89142 0.988 0.000 0.012
#> SRR1487890 1 0.0424 0.89258 0.992 0.000 0.008
#> SRR1349456 3 0.0000 0.87940 0.000 0.000 1.000
#> SRR1389384 3 0.4062 0.83008 0.000 0.164 0.836
#> SRR1316096 3 0.5178 0.76500 0.000 0.256 0.744
#> SRR1408512 1 0.5098 0.62779 0.752 0.248 0.000
#> SRR1447547 2 0.4397 0.89263 0.116 0.856 0.028
#> SRR1354053 2 0.0000 0.86513 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR815140 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1349562 1 0.0188 0.8967 0.996 0.000 0.000 0.004
#> SRR1353376 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1499040 1 0.4981 0.2218 0.536 0.000 0.464 0.000
#> SRR1322312 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1324412 3 0.1824 0.9058 0.004 0.000 0.936 0.060
#> SRR1100991 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1349479 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1431248 2 0.7050 0.4217 0.180 0.568 0.000 0.252
#> SRR1405054 3 0.2408 0.8530 0.104 0.000 0.896 0.000
#> SRR1312266 4 0.0469 0.9343 0.012 0.000 0.000 0.988
#> SRR1409790 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1352507 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1383763 1 0.3219 0.7627 0.836 0.000 0.000 0.164
#> SRR1468314 2 0.2216 0.8276 0.000 0.908 0.092 0.000
#> SRR1473674 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1390499 1 0.0817 0.8856 0.976 0.000 0.000 0.024
#> SRR821043 4 0.1118 0.9196 0.000 0.036 0.000 0.964
#> SRR1455653 4 0.1389 0.9095 0.000 0.048 0.000 0.952
#> SRR1335236 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1095383 2 0.6315 0.1447 0.000 0.508 0.060 0.432
#> SRR1479489 1 0.4985 0.2146 0.532 0.000 0.468 0.000
#> SRR1310433 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1073435 2 0.7583 0.1462 0.128 0.472 0.016 0.384
#> SRR659649 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1395999 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1105248 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1338257 4 0.3726 0.7040 0.212 0.000 0.000 0.788
#> SRR1499395 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1350002 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1489757 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1414637 2 0.0188 0.8930 0.004 0.996 0.000 0.000
#> SRR1478113 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1322477 2 0.1109 0.8785 0.004 0.968 0.000 0.028
#> SRR1478789 3 0.4994 0.0942 0.000 0.480 0.520 0.000
#> SRR1414185 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1069141 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1376852 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1323491 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1338103 1 0.0188 0.8968 0.996 0.000 0.000 0.004
#> SRR1472012 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1340325 1 0.0188 0.8968 0.996 0.000 0.004 0.000
#> SRR1087321 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1488790 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1334866 2 0.0779 0.8871 0.004 0.980 0.016 0.000
#> SRR1089446 3 0.1488 0.9257 0.032 0.012 0.956 0.000
#> SRR1344445 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1412969 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1071668 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1075804 4 0.4605 0.4711 0.336 0.000 0.000 0.664
#> SRR1383283 3 0.4535 0.5842 0.004 0.292 0.704 0.000
#> SRR1350239 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1353878 1 0.0188 0.8967 0.996 0.000 0.000 0.004
#> SRR1375721 1 0.0336 0.8948 0.992 0.000 0.008 0.000
#> SRR1083983 1 0.0592 0.8904 0.984 0.000 0.016 0.000
#> SRR1090095 1 0.0336 0.8950 0.992 0.000 0.000 0.008
#> SRR1414792 1 0.0188 0.8967 0.996 0.000 0.000 0.004
#> SRR1075102 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1098737 4 0.0707 0.9294 0.020 0.000 0.000 0.980
#> SRR1349409 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1413008 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1407179 3 0.0336 0.9502 0.008 0.000 0.992 0.000
#> SRR1095913 3 0.1302 0.9256 0.000 0.044 0.956 0.000
#> SRR1403544 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1490546 1 0.4643 0.4648 0.656 0.000 0.000 0.344
#> SRR807971 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1436228 2 0.3569 0.7096 0.196 0.804 0.000 0.000
#> SRR1445218 2 0.0336 0.8911 0.000 0.992 0.000 0.008
#> SRR1485438 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1358143 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1328760 1 0.3942 0.6834 0.764 0.000 0.236 0.000
#> SRR1380806 1 0.0707 0.8878 0.980 0.000 0.020 0.000
#> SRR1379426 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1087007 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1086256 2 0.0336 0.8914 0.008 0.992 0.000 0.000
#> SRR1346734 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1414515 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1082151 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1349320 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1317554 4 0.1118 0.9195 0.000 0.036 0.000 0.964
#> SRR1076022 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1339573 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1455878 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1446203 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1387397 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1402590 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1317532 1 0.2868 0.7948 0.864 0.000 0.000 0.136
#> SRR1331488 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1499675 1 0.9222 -0.1235 0.364 0.204 0.092 0.340
#> SRR1440467 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR807995 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1476485 4 0.0000 0.9405 0.000 0.000 0.000 1.000
#> SRR1388214 1 0.0188 0.8968 0.996 0.000 0.004 0.000
#> SRR1456051 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1473275 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1444083 1 0.6133 0.6101 0.672 0.000 0.204 0.124
#> SRR1313807 3 0.3356 0.7752 0.000 0.176 0.824 0.000
#> SRR1470751 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1403434 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1390540 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1093861 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1325290 1 0.4761 0.3904 0.628 0.372 0.000 0.000
#> SRR1070689 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1384049 1 0.4331 0.6047 0.712 0.000 0.000 0.288
#> SRR1081184 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1324295 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1365313 2 0.1004 0.8817 0.004 0.972 0.024 0.000
#> SRR1321877 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR815711 3 0.0188 0.9528 0.004 0.000 0.996 0.000
#> SRR1433476 3 0.5309 0.7018 0.000 0.164 0.744 0.092
#> SRR1101883 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1433729 2 0.3390 0.7785 0.000 0.852 0.132 0.016
#> SRR1341877 1 0.3172 0.7693 0.840 0.000 0.000 0.160
#> SRR1090556 1 0.3117 0.8156 0.880 0.028 0.092 0.000
#> SRR1357389 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1404227 3 0.1305 0.9298 0.004 0.036 0.960 0.000
#> SRR1376830 1 0.0188 0.8967 0.996 0.000 0.000 0.004
#> SRR1500661 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1080294 2 0.5125 0.3295 0.000 0.604 0.388 0.008
#> SRR1336314 4 0.0188 0.9389 0.000 0.004 0.000 0.996
#> SRR1102152 1 0.4866 0.3901 0.596 0.000 0.404 0.000
#> SRR1345244 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1478637 2 0.0707 0.8850 0.000 0.980 0.020 0.000
#> SRR1443776 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1120939 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1080117 3 0.0000 0.9556 0.000 0.000 1.000 0.000
#> SRR1102899 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1091865 2 0.4222 0.5702 0.272 0.728 0.000 0.000
#> SRR1361072 1 0.0469 0.8930 0.988 0.000 0.012 0.000
#> SRR1487890 1 0.0000 0.8980 1.000 0.000 0.000 0.000
#> SRR1349456 3 0.2081 0.8855 0.000 0.084 0.916 0.000
#> SRR1389384 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1316096 2 0.0000 0.8946 0.000 1.000 0.000 0.000
#> SRR1408512 1 0.2814 0.7996 0.868 0.000 0.000 0.132
#> SRR1447547 4 0.0188 0.9382 0.000 0.000 0.004 0.996
#> SRR1354053 4 0.3907 0.6675 0.000 0.232 0.000 0.768
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR815140 1 0.2690 0.77361 0.844 0.000 0.000 0.000 0.156
#> SRR1349562 1 0.0609 0.82115 0.980 0.000 0.000 0.000 0.020
#> SRR1353376 4 0.1197 0.89863 0.000 0.000 0.000 0.952 0.048
#> SRR1499040 3 0.4442 0.57638 0.284 0.000 0.688 0.000 0.028
#> SRR1322312 1 0.0880 0.82007 0.968 0.000 0.000 0.000 0.032
#> SRR1324412 3 0.3134 0.79490 0.012 0.000 0.864 0.096 0.028
#> SRR1100991 3 0.0880 0.87849 0.000 0.000 0.968 0.000 0.032
#> SRR1349479 3 0.0510 0.88357 0.000 0.000 0.984 0.000 0.016
#> SRR1431248 5 0.6191 0.53406 0.132 0.036 0.008 0.164 0.660
#> SRR1405054 3 0.5891 0.27163 0.120 0.000 0.552 0.000 0.328
#> SRR1312266 4 0.0865 0.90575 0.024 0.000 0.000 0.972 0.004
#> SRR1409790 3 0.0703 0.87924 0.000 0.000 0.976 0.000 0.024
#> SRR1352507 3 0.0671 0.88170 0.000 0.000 0.980 0.004 0.016
#> SRR1383763 1 0.3813 0.70307 0.800 0.000 0.008 0.164 0.028
#> SRR1468314 5 0.3285 0.69188 0.000 0.044 0.092 0.008 0.856
#> SRR1473674 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1390499 1 0.0955 0.81491 0.968 0.000 0.000 0.028 0.004
#> SRR821043 4 0.2997 0.79528 0.000 0.012 0.000 0.840 0.148
#> SRR1455653 4 0.1281 0.90320 0.000 0.012 0.000 0.956 0.032
#> SRR1335236 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1095383 5 0.8055 0.36256 0.000 0.144 0.212 0.208 0.436
#> SRR1479489 1 0.4825 0.29526 0.568 0.000 0.408 0.000 0.024
#> SRR1310433 5 0.4522 0.24323 0.000 0.440 0.008 0.000 0.552
#> SRR1073435 5 0.1413 0.70606 0.020 0.000 0.012 0.012 0.956
#> SRR659649 3 0.2172 0.84077 0.016 0.000 0.908 0.000 0.076
#> SRR1395999 1 0.3143 0.74042 0.796 0.000 0.000 0.000 0.204
#> SRR1105248 4 0.0000 0.91572 0.000 0.000 0.000 1.000 0.000
#> SRR1338257 4 0.4743 0.35207 0.332 0.000 0.004 0.640 0.024
#> SRR1499395 3 0.0290 0.88571 0.000 0.000 0.992 0.000 0.008
#> SRR1350002 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1489757 3 0.0510 0.88241 0.000 0.000 0.984 0.000 0.016
#> SRR1414637 2 0.2484 0.87540 0.028 0.900 0.000 0.004 0.068
#> SRR1478113 4 0.0000 0.91572 0.000 0.000 0.000 1.000 0.000
#> SRR1322477 5 0.6056 0.37401 0.132 0.348 0.000 0.000 0.520
#> SRR1478789 3 0.4273 0.19265 0.000 0.448 0.552 0.000 0.000
#> SRR1414185 3 0.0290 0.88571 0.000 0.000 0.992 0.000 0.008
#> SRR1069141 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1376852 1 0.2377 0.79242 0.872 0.000 0.000 0.000 0.128
#> SRR1323491 1 0.1908 0.81027 0.908 0.000 0.000 0.000 0.092
#> SRR1338103 1 0.4074 0.51726 0.636 0.000 0.000 0.000 0.364
#> SRR1472012 5 0.3143 0.61002 0.204 0.000 0.000 0.000 0.796
#> SRR1340325 1 0.0000 0.82108 1.000 0.000 0.000 0.000 0.000
#> SRR1087321 3 0.0324 0.88581 0.000 0.004 0.992 0.000 0.004
#> SRR1488790 1 0.3816 0.65323 0.696 0.000 0.000 0.000 0.304
#> SRR1334866 5 0.3803 0.69370 0.020 0.072 0.064 0.004 0.840
#> SRR1089446 5 0.3099 0.69635 0.028 0.000 0.124 0.000 0.848
#> SRR1344445 3 0.0609 0.88218 0.000 0.000 0.980 0.000 0.020
#> SRR1412969 3 0.0404 0.88511 0.000 0.000 0.988 0.000 0.012
#> SRR1071668 3 0.0609 0.88391 0.000 0.000 0.980 0.000 0.020
#> SRR1075804 1 0.5815 0.55116 0.592 0.000 0.000 0.272 0.136
#> SRR1383283 5 0.1831 0.70465 0.000 0.004 0.076 0.000 0.920
#> SRR1350239 4 0.0162 0.91473 0.000 0.000 0.004 0.996 0.000
#> SRR1353878 1 0.1267 0.81345 0.960 0.000 0.004 0.012 0.024
#> SRR1375721 5 0.4604 0.11924 0.428 0.000 0.012 0.000 0.560
#> SRR1083983 5 0.4549 -0.13182 0.464 0.000 0.008 0.000 0.528
#> SRR1090095 1 0.0912 0.82243 0.972 0.000 0.000 0.012 0.016
#> SRR1414792 1 0.0000 0.82108 1.000 0.000 0.000 0.000 0.000
#> SRR1075102 4 0.0000 0.91572 0.000 0.000 0.000 1.000 0.000
#> SRR1098737 4 0.1331 0.89282 0.040 0.000 0.000 0.952 0.008
#> SRR1349409 1 0.2074 0.80867 0.896 0.000 0.000 0.000 0.104
#> SRR1413008 4 0.0162 0.91473 0.000 0.000 0.004 0.996 0.000
#> SRR1407179 3 0.4452 -0.08802 0.004 0.000 0.500 0.000 0.496
#> SRR1095913 3 0.0771 0.87942 0.000 0.020 0.976 0.000 0.004
#> SRR1403544 1 0.1952 0.80483 0.912 0.000 0.004 0.000 0.084
#> SRR1490546 1 0.5549 0.60492 0.632 0.000 0.000 0.244 0.124
#> SRR807971 3 0.0510 0.88553 0.000 0.000 0.984 0.000 0.016
#> SRR1436228 5 0.2548 0.70396 0.072 0.028 0.004 0.000 0.896
#> SRR1445218 5 0.4574 0.30462 0.000 0.412 0.000 0.012 0.576
#> SRR1485438 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1358143 1 0.0510 0.82203 0.984 0.000 0.000 0.000 0.016
#> SRR1328760 1 0.5105 0.53344 0.660 0.000 0.264 0.000 0.076
#> SRR1380806 1 0.2293 0.76799 0.900 0.000 0.084 0.000 0.016
#> SRR1379426 3 0.0290 0.88445 0.000 0.000 0.992 0.000 0.008
#> SRR1087007 3 0.0404 0.88490 0.000 0.000 0.988 0.000 0.012
#> SRR1086256 5 0.2179 0.68085 0.000 0.100 0.004 0.000 0.896
#> SRR1346734 4 0.0290 0.91548 0.000 0.000 0.000 0.992 0.008
#> SRR1414515 1 0.4288 0.51828 0.612 0.000 0.004 0.000 0.384
#> SRR1082151 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1349320 4 0.0290 0.91592 0.000 0.000 0.000 0.992 0.008
#> SRR1317554 4 0.4367 0.39192 0.000 0.008 0.000 0.620 0.372
#> SRR1076022 5 0.3636 0.56075 0.000 0.272 0.000 0.000 0.728
#> SRR1339573 3 0.0510 0.88246 0.000 0.000 0.984 0.000 0.016
#> SRR1455878 1 0.3177 0.74752 0.792 0.000 0.000 0.000 0.208
#> SRR1446203 3 0.1740 0.85798 0.012 0.000 0.932 0.000 0.056
#> SRR1387397 5 0.2771 0.68201 0.128 0.000 0.012 0.000 0.860
#> SRR1402590 1 0.0703 0.82133 0.976 0.000 0.000 0.000 0.024
#> SRR1317532 5 0.4171 0.16669 0.396 0.000 0.000 0.000 0.604
#> SRR1331488 4 0.0000 0.91572 0.000 0.000 0.000 1.000 0.000
#> SRR1499675 5 0.1557 0.70302 0.052 0.000 0.000 0.008 0.940
#> SRR1440467 3 0.1121 0.86728 0.000 0.000 0.956 0.000 0.044
#> SRR807995 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1476485 4 0.1478 0.88799 0.000 0.000 0.000 0.936 0.064
#> SRR1388214 5 0.3300 0.59443 0.204 0.000 0.004 0.000 0.792
#> SRR1456051 1 0.2127 0.80285 0.892 0.000 0.000 0.000 0.108
#> SRR1473275 3 0.0693 0.88442 0.008 0.000 0.980 0.000 0.012
#> SRR1444083 3 0.6913 -0.00814 0.392 0.000 0.432 0.148 0.028
#> SRR1313807 5 0.3635 0.60068 0.000 0.000 0.248 0.004 0.748
#> SRR1470751 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1403434 3 0.1197 0.86543 0.000 0.000 0.952 0.000 0.048
#> SRR1390540 5 0.4305 -0.15692 0.488 0.000 0.000 0.000 0.512
#> SRR1093861 2 0.0162 0.96273 0.000 0.996 0.000 0.000 0.004
#> SRR1325290 5 0.2329 0.67322 0.124 0.000 0.000 0.000 0.876
#> SRR1070689 1 0.2020 0.81074 0.900 0.000 0.000 0.000 0.100
#> SRR1384049 1 0.4717 0.53287 0.660 0.000 0.004 0.308 0.028
#> SRR1081184 1 0.0794 0.82105 0.972 0.000 0.000 0.000 0.028
#> SRR1324295 1 0.0000 0.82108 1.000 0.000 0.000 0.000 0.000
#> SRR1365313 5 0.3743 0.67989 0.004 0.096 0.076 0.000 0.824
#> SRR1321877 3 0.0510 0.88515 0.000 0.000 0.984 0.000 0.016
#> SRR815711 5 0.4294 0.14993 0.000 0.000 0.468 0.000 0.532
#> SRR1433476 5 0.3304 0.67128 0.000 0.000 0.168 0.016 0.816
#> SRR1101883 3 0.0290 0.88571 0.000 0.000 0.992 0.000 0.008
#> SRR1433729 5 0.6207 0.51088 0.000 0.124 0.264 0.020 0.592
#> SRR1341877 5 0.2280 0.67267 0.120 0.000 0.000 0.000 0.880
#> SRR1090556 5 0.1410 0.69891 0.060 0.000 0.000 0.000 0.940
#> SRR1357389 3 0.0162 0.88594 0.000 0.000 0.996 0.000 0.004
#> SRR1404227 5 0.4138 0.36912 0.000 0.000 0.384 0.000 0.616
#> SRR1376830 1 0.0290 0.82147 0.992 0.000 0.000 0.000 0.008
#> SRR1500661 1 0.4182 0.48877 0.600 0.000 0.000 0.000 0.400
#> SRR1080294 5 0.4677 0.60154 0.000 0.036 0.236 0.012 0.716
#> SRR1336314 4 0.0404 0.91330 0.000 0.012 0.000 0.988 0.000
#> SRR1102152 3 0.4993 0.67835 0.168 0.056 0.740 0.000 0.036
#> SRR1345244 3 0.0290 0.88571 0.000 0.000 0.992 0.000 0.008
#> SRR1478637 2 0.0671 0.95012 0.000 0.980 0.016 0.000 0.004
#> SRR1443776 3 0.0290 0.88598 0.000 0.000 0.992 0.000 0.008
#> SRR1120939 3 0.0290 0.88571 0.000 0.000 0.992 0.000 0.008
#> SRR1080117 3 0.0290 0.88445 0.000 0.000 0.992 0.000 0.008
#> SRR1102899 5 0.4632 0.24091 0.000 0.448 0.012 0.000 0.540
#> SRR1091865 2 0.0703 0.94205 0.024 0.976 0.000 0.000 0.000
#> SRR1361072 1 0.4603 0.63729 0.668 0.000 0.032 0.000 0.300
#> SRR1487890 1 0.0609 0.82185 0.980 0.000 0.000 0.000 0.020
#> SRR1349456 3 0.4273 0.09141 0.000 0.000 0.552 0.000 0.448
#> SRR1389384 2 0.0000 0.96546 0.000 1.000 0.000 0.000 0.000
#> SRR1316096 2 0.3274 0.64926 0.000 0.780 0.000 0.000 0.220
#> SRR1408512 5 0.3928 0.43661 0.296 0.000 0.000 0.004 0.700
#> SRR1447547 4 0.0162 0.91581 0.000 0.000 0.000 0.996 0.004
#> SRR1354053 4 0.2561 0.79466 0.000 0.144 0.000 0.856 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR815140 1 0.2163 0.7815 0.892 0.000 0.000 0.004 0.096 0.008
#> SRR1349562 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1353376 4 0.2994 0.7217 0.000 0.000 0.000 0.788 0.004 0.208
#> SRR1499040 3 0.3861 0.5314 0.316 0.004 0.672 0.000 0.008 0.000
#> SRR1322312 1 0.0146 0.8161 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1324412 3 0.1624 0.8798 0.012 0.000 0.936 0.044 0.008 0.000
#> SRR1100991 3 0.1851 0.8763 0.024 0.000 0.928 0.000 0.012 0.036
#> SRR1349479 3 0.1485 0.8946 0.000 0.000 0.944 0.004 0.024 0.028
#> SRR1431248 5 0.1053 0.6774 0.020 0.000 0.012 0.004 0.964 0.000
#> SRR1405054 5 0.3509 0.5988 0.016 0.000 0.240 0.000 0.744 0.000
#> SRR1312266 4 0.1049 0.8794 0.032 0.000 0.000 0.960 0.008 0.000
#> SRR1409790 3 0.0260 0.9066 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1352507 3 0.0820 0.9056 0.000 0.000 0.972 0.016 0.012 0.000
#> SRR1383763 1 0.2320 0.7282 0.864 0.000 0.004 0.132 0.000 0.000
#> SRR1468314 6 0.0508 0.7421 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1473674 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1390499 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR821043 4 0.2527 0.7784 0.000 0.000 0.000 0.832 0.000 0.168
#> SRR1455653 4 0.0363 0.8998 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1335236 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1095383 6 0.6549 0.4876 0.000 0.072 0.152 0.216 0.008 0.552
#> SRR1479489 1 0.4877 0.2620 0.560 0.000 0.388 0.000 0.012 0.040
#> SRR1310433 6 0.4153 0.4521 0.000 0.340 0.000 0.000 0.024 0.636
#> SRR1073435 6 0.1863 0.6897 0.000 0.000 0.000 0.000 0.104 0.896
#> SRR659649 5 0.3330 0.5287 0.000 0.000 0.284 0.000 0.716 0.000
#> SRR1395999 1 0.2179 0.7923 0.900 0.000 0.000 0.000 0.064 0.036
#> SRR1105248 4 0.0146 0.9010 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1338257 4 0.3742 0.4089 0.348 0.000 0.004 0.648 0.000 0.000
#> SRR1499395 3 0.0508 0.9074 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1350002 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489757 3 0.0713 0.9044 0.000 0.000 0.972 0.000 0.028 0.000
#> SRR1414637 2 0.1411 0.9086 0.004 0.936 0.000 0.000 0.000 0.060
#> SRR1478113 4 0.0000 0.9013 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1322477 5 0.5464 0.3087 0.072 0.328 0.000 0.012 0.576 0.012
#> SRR1478789 3 0.4090 0.4078 0.000 0.384 0.604 0.000 0.004 0.008
#> SRR1414185 3 0.0146 0.9069 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1069141 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1376852 1 0.2402 0.7501 0.856 0.000 0.000 0.000 0.140 0.004
#> SRR1323491 1 0.2094 0.7918 0.908 0.000 0.000 0.004 0.064 0.024
#> SRR1338103 1 0.5695 0.3236 0.544 0.000 0.000 0.004 0.192 0.260
#> SRR1472012 5 0.5988 0.1495 0.348 0.000 0.000 0.000 0.416 0.236
#> SRR1340325 1 0.0146 0.8151 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1087321 3 0.0993 0.9041 0.000 0.012 0.964 0.000 0.024 0.000
#> SRR1488790 5 0.2264 0.6732 0.096 0.000 0.012 0.004 0.888 0.000
#> SRR1334866 6 0.1285 0.7465 0.000 0.000 0.052 0.000 0.004 0.944
#> SRR1089446 6 0.1858 0.7378 0.000 0.000 0.092 0.000 0.004 0.904
#> SRR1344445 3 0.0777 0.9062 0.000 0.004 0.972 0.000 0.024 0.000
#> SRR1412969 3 0.1333 0.8822 0.000 0.000 0.944 0.000 0.008 0.048
#> SRR1071668 3 0.1267 0.8916 0.000 0.000 0.940 0.000 0.060 0.000
#> SRR1075804 1 0.5113 0.5896 0.684 0.000 0.000 0.108 0.032 0.176
#> SRR1383283 6 0.0291 0.7380 0.000 0.000 0.004 0.000 0.004 0.992
#> SRR1350239 4 0.0146 0.9010 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1353878 1 0.0508 0.8094 0.984 0.000 0.012 0.000 0.004 0.000
#> SRR1375721 1 0.4843 0.5146 0.664 0.000 0.000 0.000 0.192 0.144
#> SRR1083983 1 0.5470 0.3884 0.580 0.000 0.004 0.000 0.256 0.160
#> SRR1090095 1 0.0870 0.8159 0.972 0.000 0.000 0.004 0.012 0.012
#> SRR1414792 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1075102 4 0.0000 0.9013 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1098737 4 0.2869 0.7720 0.020 0.000 0.000 0.832 0.148 0.000
#> SRR1349409 1 0.1367 0.8076 0.944 0.000 0.000 0.000 0.012 0.044
#> SRR1413008 4 0.0146 0.9010 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1407179 6 0.3937 0.3565 0.000 0.000 0.424 0.000 0.004 0.572
#> SRR1095913 3 0.2258 0.8568 0.000 0.060 0.896 0.000 0.000 0.044
#> SRR1403544 1 0.3996 0.0278 0.512 0.000 0.004 0.000 0.484 0.000
#> SRR1490546 1 0.5573 0.3817 0.572 0.000 0.000 0.200 0.224 0.004
#> SRR807971 3 0.1007 0.8995 0.000 0.000 0.956 0.000 0.044 0.000
#> SRR1436228 6 0.1320 0.7266 0.000 0.016 0.000 0.000 0.036 0.948
#> SRR1445218 6 0.2527 0.6766 0.000 0.168 0.000 0.000 0.000 0.832
#> SRR1485438 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1358143 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1328760 5 0.3830 0.6390 0.044 0.000 0.212 0.000 0.744 0.000
#> SRR1380806 1 0.2257 0.7119 0.876 0.000 0.116 0.000 0.008 0.000
#> SRR1379426 3 0.1453 0.8851 0.000 0.000 0.944 0.008 0.008 0.040
#> SRR1087007 3 0.0717 0.9022 0.000 0.000 0.976 0.000 0.008 0.016
#> SRR1086256 6 0.0363 0.7383 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR1346734 4 0.0146 0.9012 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1414515 5 0.2907 0.6457 0.152 0.000 0.020 0.000 0.828 0.000
#> SRR1082151 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1349320 4 0.0622 0.8975 0.000 0.000 0.000 0.980 0.012 0.008
#> SRR1317554 4 0.3647 0.4523 0.000 0.000 0.000 0.640 0.000 0.360
#> SRR1076022 6 0.4672 0.3853 0.000 0.348 0.000 0.000 0.056 0.596
#> SRR1339573 3 0.0436 0.9057 0.000 0.004 0.988 0.000 0.004 0.004
#> SRR1455878 1 0.4788 0.1871 0.548 0.000 0.056 0.000 0.396 0.000
#> SRR1446203 5 0.3797 0.2486 0.000 0.000 0.420 0.000 0.580 0.000
#> SRR1387397 6 0.0881 0.7408 0.008 0.000 0.012 0.000 0.008 0.972
#> SRR1402590 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1317532 5 0.1637 0.6808 0.056 0.000 0.004 0.004 0.932 0.004
#> SRR1331488 4 0.0000 0.9013 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1499675 6 0.1141 0.7208 0.000 0.000 0.000 0.000 0.052 0.948
#> SRR1440467 3 0.1462 0.8887 0.000 0.000 0.936 0.000 0.056 0.008
#> SRR807995 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476485 4 0.0260 0.9008 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1388214 6 0.3827 0.4456 0.256 0.000 0.004 0.000 0.020 0.720
#> SRR1456051 1 0.0935 0.8130 0.964 0.000 0.000 0.000 0.032 0.004
#> SRR1473275 3 0.0790 0.9024 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1444083 3 0.5526 0.2803 0.356 0.000 0.524 0.112 0.008 0.000
#> SRR1313807 6 0.1895 0.7419 0.000 0.000 0.072 0.000 0.016 0.912
#> SRR1470751 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1403434 3 0.2006 0.8564 0.000 0.000 0.904 0.000 0.016 0.080
#> SRR1390540 5 0.5373 0.1408 0.384 0.000 0.000 0.004 0.512 0.100
#> SRR1093861 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1325290 6 0.5972 -0.0906 0.268 0.000 0.000 0.000 0.284 0.448
#> SRR1070689 1 0.1462 0.8036 0.936 0.000 0.000 0.000 0.008 0.056
#> SRR1384049 1 0.3547 0.5381 0.696 0.000 0.004 0.300 0.000 0.000
#> SRR1081184 1 0.0000 0.8158 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324295 1 0.0146 0.8165 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1365313 6 0.1477 0.7472 0.000 0.004 0.048 0.000 0.008 0.940
#> SRR1321877 3 0.1010 0.9022 0.000 0.004 0.960 0.000 0.036 0.000
#> SRR815711 6 0.3955 0.4421 0.000 0.000 0.384 0.000 0.008 0.608
#> SRR1433476 6 0.1196 0.7460 0.000 0.000 0.040 0.000 0.008 0.952
#> SRR1101883 3 0.0146 0.9069 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1433729 6 0.2738 0.6915 0.000 0.000 0.176 0.000 0.004 0.820
#> SRR1341877 5 0.4560 0.5376 0.088 0.000 0.000 0.004 0.696 0.212
#> SRR1090556 6 0.2355 0.6882 0.008 0.000 0.004 0.000 0.112 0.876
#> SRR1357389 3 0.0458 0.9072 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1404227 6 0.2996 0.6534 0.000 0.000 0.228 0.000 0.000 0.772
#> SRR1376830 1 0.1082 0.8103 0.956 0.000 0.000 0.004 0.040 0.000
#> SRR1500661 5 0.4178 0.1081 0.428 0.000 0.000 0.004 0.560 0.008
#> SRR1080294 6 0.2163 0.7356 0.000 0.000 0.092 0.000 0.016 0.892
#> SRR1336314 4 0.0547 0.8952 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1102152 3 0.4671 0.5540 0.268 0.028 0.676 0.000 0.012 0.016
#> SRR1345244 3 0.0603 0.9068 0.000 0.004 0.980 0.000 0.016 0.000
#> SRR1478637 2 0.0622 0.9479 0.000 0.980 0.012 0.000 0.008 0.000
#> SRR1443776 3 0.1349 0.8922 0.000 0.004 0.940 0.000 0.056 0.000
#> SRR1120939 3 0.0858 0.9050 0.000 0.004 0.968 0.000 0.028 0.000
#> SRR1080117 3 0.0436 0.9057 0.000 0.004 0.988 0.000 0.004 0.004
#> SRR1102899 6 0.4570 0.3726 0.000 0.376 0.008 0.000 0.028 0.588
#> SRR1091865 2 0.0146 0.9615 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1361072 5 0.2364 0.6858 0.072 0.000 0.032 0.004 0.892 0.000
#> SRR1487890 1 0.0146 0.8152 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1349456 6 0.4294 0.3248 0.000 0.000 0.428 0.000 0.020 0.552
#> SRR1389384 2 0.0000 0.9651 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1316096 2 0.3288 0.5472 0.000 0.724 0.000 0.000 0.000 0.276
#> SRR1408512 6 0.6048 -0.0627 0.352 0.000 0.000 0.012 0.176 0.460
#> SRR1447547 4 0.0146 0.9010 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1354053 4 0.2320 0.8085 0.000 0.132 0.000 0.864 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0